System and method for flight control in electric aircraft

ABSTRACT

A system for flight control in electric aircraft includes a flight controller configured to provide an initial vehicle torque signal including a plurality of attitude commands. The system includes a mixer configured to receive the initial vehicle torque signal and a vehicle torque limit, receive prioritization data including a prioritization datum corresponding to each of the plurality of attitude command, determine a plurality of modified attitude commands as a function of the vehicle torque limit, the attitude commands, and the prioritization data, generate, as a function of modified attitude commands, an output torque command including the initial vehicle torque signal adjusted as a function of the vehicle torque limit, generate, as a function of the output torque command, a remaining vehicle torque. The system includes a display, wherein the display is configured to present, to a user, the remaining vehicle torque and the output torque command.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation-in-part of Non-provisionalapplication Ser. No. 17/197,427 filed on Mar. 10, 2021 and entitled“SYSTEM AND METHOD FOR FLIGHT CONTROL IN ELECTRIC AIRCRAFT,” theentirety of which is incorporated herein by reference.

FIELD OF THE INVENTION

The present invention generally relates to the field of electricaircraft. In particular, the present invention is directed to system andmethod for flight control in electric aircraft.

BACKGROUND

The burgeoning of electric vertical take-off and landing (eVTOL)aircraft technologies promises an unprecedented forward leap in energyefficiency, cost savings, and the potential of future autonomous andunmanned aircraft. However, the technology of eVTOL aircraft is stilllacking in crucial areas of control. This is particularly problematic asit compounds the already daunting challenges to designers andmanufacturers developing the aircraft for manned and/or unmanned flightin the real world.

SUMMARY OF THE DISCLOSURE

In an aspect, a system for flight control in electric aircraft includesa flight controller, wherein the flight controller is configured toprovide an initial vehicle torque signal comprising a plurality ofattitude commands. The system includes a mixer, wherein the mixerincludes circuitry configured to receive the initial vehicle torquesignal, receive at least a vehicle torque limit, receive a plurality ofprioritization data, the plurality of prioritization data including aprioritization datum corresponding to each of the plurality of attitudecommand, determine a plurality of modified attitude commands as afunction of the at least a vehicle torque limit, the plurality ofattitude commands, and the plurality of prioritization data, generate,as a function of modified attitude commands, an output torque command,wherein the output torque command includes the initial vehicle torquesignal adjusted as a function of the at least a vehicle torque limit,generate, as a function of the output torque command, a remainingvehicle torque. The system includes a display, wherein the display isconfigured to present, to a user, the remaining vehicle torque and theoutput torque command.

In another aspect, a method for flight control in electric aircraftincludes providing, at the flight controller, an initial vehicle torquesignal comprising at least an attitude command, receiving, at the mixer,the initial vehicle torque signal including a plurality of attitudecommands, receiving, at the mixer, at least a vehicle torque limit,receiving, at the mixer, a plurality of prioritization data including aprioritization datum corresponding to each of the plurality of attitudecommands, determining, at the mixer, a plurality of modified attitudecommands as a function of the at least a vehicle torque limit, theplurality of attitude commands, and the plurality of prioritizationdata, generating, at the mixer, as a function of modified attitudecommands, an output torque command, wherein the output torque commandincludes the initial vehicle torque signal adjusted as a function of theat least a vehicle torque limit, generating, at the mixer, as a functionof the output torque command, a remaining vehicle torque, and displayingto a user the remaining vehicle torque and the output torque command.

These and other aspects and features of non-limiting embodiments of thepresent invention will become apparent to those skilled in the art uponreview of the following description of specific non-limiting embodimentsof the invention in conjunction with the accompanying drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

For the purpose of illustrating the invention, the drawings show aspectsof one or more embodiments of the invention. However, it should beunderstood that the present invention is not limited to the precisearrangements and instrumentalities shown in the drawings, wherein:

FIG. 1 is a block diagram illustrating an exemplary embodiment of asystem for flight control in electric aircraft;

FIG. 2 is a block diagram illustrating an exemplary embodiment of amixer and sequential problems solved therein.

FIGS. 3A and 3B are graphical representations illustrating the hereindisclosed system for torque allocation in an electric aircraft, withoutand with inertial compensation, respectively;

FIG. 4 is a block diagram of an exemplary flight controller;

FIG. 5 is a flow chart of an exemplary embodiment of a method of flightcontrol in electric aircraft;

FIG. 6 is a block diagram illustrating an exemplary embodiment of amachine-learning process.

FIG. 7 is an illustration of an embodiment of an electric aircraft; and

FIG. 8 is a block diagram of a computing system that can be used toimplement any one or more of the methodologies disclosed herein and anyone or more portions thereof.

The drawings are not necessarily to scale and may be illustrated byphantom lines, diagrammatic representations and fragmentary views. Incertain instances, details that are not necessary for an understandingof the embodiments or that render other details difficult to perceivemay have been omitted.

DETAILED DESCRIPTION

In the following description, for the purposes of explanation, numerousspecific details are set forth in order to provide a thoroughunderstanding of the present invention. It will be apparent, however,that the present invention may be practiced without these specificdetails. As used herein, the word “exemplary” or “illustrative” means“serving as an example, instance, or illustration.” Any implementationdescribed herein as “exemplary” or “illustrative” is not necessarily tobe construed as preferred or advantageous over other implementations.All of the implementations described below are exemplary implementationsprovided to enable persons skilled in the art to make or use theembodiments of the disclosure and are not intended to limit the scope ofthe disclosure, which is defined by the claims. For purposes ofdescription herein, the terms “upper”, “lower”, “left”, “rear”, “right”,“front”, “vertical”, “horizontal”, and derivatives thereof shall relateto embodiments oriented as shown for exemplary purposes in FIG. 8.Furthermore, there is no intention to be bound by any expressed orimplied theory presented in the preceding technical field, background,brief summary or the following detailed description. It is also to beunderstood that the specific devices and processes illustrated in theattached drawings, and described in the following specification, aresimply embodiments of the inventive concepts defined in the appendedclaims. Hence, specific dimensions and other physical characteristicsrelating to the embodiments disclosed herein are not to be considered aslimiting, unless the claims expressly state otherwise.

Still referring FIG. 1, system 100 may include a computing device, whichmay include any computing device as described in this disclosure,including without limitation a microcontroller, microprocessor, digitalsignal processor (DSP) and/or system on a chip (SoC) as described inthis disclosure. Computing device may include, be included in, and/orcommunicate with a mobile device such as a mobile telephone orsmartphone. Computing device may include a single computing deviceoperating independently, or may include two or more computing deviceoperating in concert, in parallel, sequentially or the like; two or morecomputing devices may be included together in a single computing deviceor in two or more computing devices. Computing Device may interface orcommunicate with one or more additional devices as described below infurther detail via a network interface device. Network interface devicemay be utilized for connecting computing device to one or more of avariety of networks, and one or more devices. Examples of a networkinterface device include, but are not limited to, a network interfacecard (e.g., a mobile network interface card, a LAN card), a modem, andany combination thereof. Examples of a network include, but are notlimited to, a wide area network (e.g., the Internet, an enterprisenetwork), a local area network (e.g., a network associated with anoffice, a building, a campus or other relatively small geographicspace), a telephone network, a data network associated with atelephone/voice provider (e.g., a mobile communications provider dataand/or voice network), a direct connection between two computingdevices, and any combinations thereof. A network may employ a wiredand/or a wireless mode of communication. In general, any networktopology may be used. Information (e.g., data, software etc.) may becommunicated to and/or from a computer and/or a computing device.Computing device may include but is not limited to, for example, acomputing device or cluster of computing devices in a first location anda second computing device or cluster of computing devices in a secondlocation. Computing device may include one or more computing devicesdedicated to data storage, security, distribution of traffic for loadbalancing, and the like. Computing device may distribute one or morecomputing tasks as described below across a plurality of computingdevices of computing device, which may operate in parallel, in series,redundantly, or in any other manner used for distribution of tasks ormemory between computing devices. Computing device may be implementedusing a “shared nothing” architecture in which data is cached at theworker, in an embodiment, this may enable scalability of system 100and/or computing device.

Still referring to FIG. 1, computing device that may be present insystem 100 may be designed and/or configured to perform any method,method step, or sequence of method steps in any embodiment described inthis disclosure, in any order and with any degree of repetition. Forinstance, Computing device may be configured to perform a single step orsequence repeatedly until a desired or commanded outcome is achieved;repetition of a step or a sequence of steps may be performed iterativelyand/or recursively using outputs of previous repetitions as inputs tosubsequent repetitions, aggregating inputs and/or outputs of repetitionsto produce an aggregate result, reduction or decrement of one or morevariables such as global variables, and/or division of a largerprocessing task into a set of iteratively addressed smaller processingtasks. Computing device may perform any step or sequence of steps asdescribed in this disclosure in parallel, such as simultaneously and/orsubstantially simultaneously performing a step two or more times usingtwo or more parallel threads, processor cores, or the like; division oftasks between parallel threads and/or processes may be performedaccording to any protocol suitable for division of tasks betweeniterations. Persons skilled in the art, upon reviewing the entirety ofthis disclosure, will be aware of various ways in which steps, sequencesof steps, processing tasks, and/or data may be subdivided, shared, orotherwise dealt with using iteration, recursion, and/or parallelprocessing.

Still referring to FIG. 1, system 100 configured for use in electricaircraft is presented. System 100 includes flight controller 104configured to provide initial vehicle torque signal 108 for at least apropulsor. Flight controller 104 may be a computing device as previouslydisclosed. Flight controller 104 may be a processor configured tocontrol the output of a plurality of propulsors in response to inputs.Inputs to this system may include pilot manipulations of physicalcontrol interfaces, remote signals generated from electronic devices,voice commands, physiological readings like eye movements, pedalmanipulation, or a combination thereof, to name a few. Flight controller104 may include a proportional-integral-derivative (PID) controller. A“PID controller”, for the purposes of this disclosure, is a control loopmechanism employing feedback that calculates an error value as thedifference between a desired setpoint and a measured process variableand applies a correction based on proportional, integral, and derivativeterms; integral and derivative terms may be generated, respectively,using analog integrators and differentiators constructed withoperational amplifiers and/or digital integrators and differentiators,as a non-limiting example. PID controllers may automatically applyaccurate and responsive correction to a control function in a loop, suchthat over time the correction remains responsive to the previous outputand actively controls an output. Flight controller 104 may includedamping, including critical damping to attain the desired setpoint,which may be an output to a propulsor in a timely and accurate way.

Still referring to FIG. 1, flight controller 104 may be implementedconsistently with any flight controller as described herein. Flightcontroller 104 is configured to provide an initial vehicle torque signal108 comprising a plurality of attitude commands 112. Initial vehicletorque signal 108 may include a desired change in aircraft trajectory asinputted by an onboard or offboard pilot, remotely located user, one ormore computing devices such as an “autopilot” program or module, anycombination thereof, or the like. Initial vehicle torque signal 108 mayinclude without limitation one or more electrical signals, audiovisualsignals, physical indications of desired vehicle-level torques andforces, or the like. “Trajectory”, for the purposes of this disclosureis the path followed by a projectile or vehicle flying or an objectmoving under the action of given forces. Trajectory may be altered byaircraft control surfaces and/or one or more propulsors working intandem to manipulate a fluid medium in which the object is movingthrough. Initial vehicle torque signal 108 may include a signalgenerated from manipulation of a pilot input control consistent with theentirety of this disclosure.

Further referring to FIG. 1, flight controller 104 may include one ormore circuit elements communicatively coupled together. One or moresensors may be communicatively coupled to at least a pilot control, themanipulation of which, may constitute at least an aircraft command.Signals may include electrical, electromagnetic, visual, audio, radiowaves, or another undisclosed signal type alone or in combination. Atleast a sensor communicatively connected to at least a pilot control mayinclude a sensor disposed on, near, around or within at least pilotcontrol. At least a sensor may include a motion sensor. “Motion sensor”,for the purposes of this disclosure refers to a device or componentconfigured to detect physical movement of an object or grouping ofobjects. One of ordinary skill in the art would appreciate, afterreviewing the entirety of this disclosure, that motion may include aplurality of types including but not limited to: spinning, rotating,oscillating, gyrating, jumping, sliding, reciprocating, or the like. Atleast a sensor may include, torque sensor, gyroscope, accelerometer,torque sensor, magnetometer, inertial measurement unit (IMU), pressuresensor, force sensor, proximity sensor, displacement sensor, vibrationsensor, among others. At least a sensor 104 may include a sensor suitewhich may include a plurality of sensors that may detect similar orunique phenomena. For example, in a non-limiting embodiment, sensorsuite may include a plurality of accelerometers, a mixture ofaccelerometers and gyroscopes, or a mixture of an accelerometer,gyroscope, and torque sensor. The herein disclosed system and method maycomprise a plurality of sensors in the form of individual sensors or asensor suite working in tandem or individually. A sensor suite mayinclude a plurality of independent sensors, as described herein, whereany number of the described sensors may be used to detect any number ofphysical or electrical quantities associated with an aircraft powersystem or an electrical energy storage system. Independent sensors mayinclude separate sensors measuring physical or electrical quantitiesthat may be powered by and/or in communication with circuitsindependently, where each may signal sensor output to a control circuitsuch as a user graphical interface. In an embodiment, use of a pluralityof independent sensors may result in redundancy configured to employmore than one sensor that measures the same phenomenon, those sensorsbeing of the same type, a combination of, or another type of sensor notdisclosed, so that in the event one sensor fails, the ability to detectphenomenon is maintained and in a non-limiting example, a user alteraircraft usage pursuant to sensor readings.

Still referring to FIG. 1, at least a sensor may be configured to detectpilot input from at least pilot control. At least pilot control mayinclude a throttle lever, inceptor stick, collective pitch control,steering wheel, brake pedals, pedal controls, toggles, joystick. One ofordinary skill in the art, upon reading the entirety of this disclosurewould appreciate the variety of pilot input controls that may be presentin an electric aircraft consistent with the present disclosure. Inceptorstick may be consistent with disclosure of inceptor stick in U.S. patentapplication Ser. No. 17/001,845 and titled “A HOVER AND THRUST CONTROLASSEMBLY FOR DUAL-MODE AIRCRAFT”, which is incorporated herein byreference in its entirety. Collective pitch control may be consistentwith disclosure of collective pitch control in U.S. patent applicationSer. No. 16/929,206 and titled “HOVER AND THRUST CONTROL ASSEMBLY FORDUAL-MODE AIRCRAFT”, which is incorporated herein by reference in itsentirety. At least pilot control may be physically located in thecockpit of the aircraft or remotely located outside of the aircraft inanother location communicatively connected to at least a portion of theaircraft. “Communicatively connect”, for the purposes of thisdisclosure, is a process whereby one device, component, or circuit isable to receive data from and/or transmit data to another device,component, or circuit; communicative connecting may be performed bywired or wireless electronic communication, either directly or by way ofone or more intervening devices or components. In an embodiment,communicative connecting includes electrically coupling an output of onedevice, component, or circuit to an input of another device, component,or circuit. Communicative connecting may be performed via a bus or otherfacility for intercommunication between elements of a computing device.Communicative connecting may include indirect connections via “wireless”connection, low power wide area network, radio communication, opticalcommunication, magnetic, capacitive, or optical coupling, or the like.At least pilot control may include buttons, switches, or other binaryinputs in addition to, or alternatively than digital controls aboutwhich a plurality of inputs may be received. At least pilot control maybe configured to receive pilot input. Pilot input may include a physicalmanipulation of a control like a pilot using a hand and arm to push orpull a lever, or a pilot using a finger to manipulate a switch. Pilotinput may include a voice command by a pilot to a microphone andcomputing system consistent with the entirety of this disclosure. One ofordinary skill in the art, after reviewing the entirety of thisdisclosure, would appreciate that this is a non-exhaustive list ofcomponents and interactions thereof that may include, represent, orconstitute, initial vehicle torque signal 108.

With continued reference to FIG. 1, initial vehicle torque signal 108,which is provided by flight controller 104, includes a plurality ofattitude commands 112. “Attitude”, for the purposes of this disclosure,is the relative orientation of a body, in this case an electricaircraft, as compared to earth's surface or any other reference pointand/or coordinate system. Attitude is generally displayed to pilots,personnel, remote users, or one or more computing devices in an attitudeindicator, such as without limitation a visual representation of thehorizon and its relative orientation to the aircraft. Plurality ofattitude commands 112 may indicate one or more measurements relative toan aircraft's pitch, roll, yaw, or throttle compared to a relativestarting point. One or more sensors may measure or detect the aircraft'sattitude and establish one or more attitude datums. An “attitude datum”,for the purposes of this disclosure, refers to at least an element ofdata identifying and/or a pilot input or command. At least a pilotcontrol may be communicatively connected to any other componentpresented in system, the communicative connection may include redundantconnections configured to safeguard against single-point failure.Plurality of attitude commands 112 may indicate a pilot's instruction tochange the heading and/or trim of an electric aircraft. Pilot input mayindicate a pilot's instruction to change an aircraft's pitch, roll, yaw,throttle, and/or any combination thereof. Aircraft trajectory may bemanipulated by one or more control surfaces and propulsors working aloneor in tandem consistent with the entirety of this disclosure,hereinbelow. “Pitch”, for the purposes of this disclosure refers to anaircraft's angle of attack, that is the difference between theaircraft's nose and a horizontal flight trajectory. For example, anaircraft may pitch “up” when its nose is angled upward compared tohorizontal flight, as in a climb maneuver. In another example, anaircraft may pitch “down”, when its nose is angled downward compared tohorizontal flight, like in a dive maneuver. When angle of attack is notan acceptable input to any system disclosed herein, proxies may be usedsuch as pilot controls, remote controls, or sensor levels, such as trueairspeed sensors, pitot tubes, pneumatic/hydraulic sensors, and thelike. “Roll” for the purposes of this disclosure, refers to anaircraft's position about its longitudinal axis, that is to say thatwhen an aircraft rotates about its axis from its tail to its nose, andone side rolls upward, as in a banking maneuver. “Yaw”, for the purposesof this disclosure, refers to an aircraft's turn angle, when an aircraftrotates about an imaginary vertical axis intersecting the center of theearth and the fuselage of the aircraft. “Throttle”, for the purposes ofthis disclosure, refers to an aircraft outputting an amount of thrustfrom a propulsor. Pilot input, when referring to throttle, may refer toa pilot's desire to increase or decrease thrust produced by at least apropulsor. Initial vehicle torque signal 108 may include an electricalsignal. At least an aircraft command 104 may include mechanical movementof any throttle consistent with the entirety of this disclosure.Electrical signals may include analog signals, digital signals, periodicor aperiodic signal, step signals, unit impulse signal, unit rampsignal, unit parabolic signal, signum function, exponential signal,rectangular signal, triangular signal, sinusoidal signal, sinc function,or pulse width modulated signal. At least a sensor may includecircuitry, computing devices, electronic components or a combinationthereof that translates pilot input into at initial vehicle torquesignal 108 configured to be transmitted to another electronic component.Plurality of attitude commands 112 may include a total attitude commanddatum, such as a combination of attitude adjustments represented by oneor a certain number of combinatorial datums. Plurality of attitudecommands 112 may include individual attitude datums representing totalor relative change in attitude measurements relative to pitch, roll,yaw, and throttle.

With continued reference to FIG. 1, vehicle-level torque commands suchas initial vehicle torque signal 108 may be translated into propulsorcommands such as output torque command 136 through modified attitudecommands 132 in mixer 128 such that onboard electronics solve systems ofequations in pitch moment, roll moment, yaw moment, and collective forcemay send each of a plurality of propulsors signals to achieve thedesired vehicle torque. It should be noted that “collective force” mayadditionally or alternatively be called “assisted lift force” and thatthis terminology does not alter the meaning of either “collective force”or “assisted lift force” as used herein. Here, “desired vehicle torque”is directly related to initial vehicle torque signal 108 consistent withthe disclosure. It should be noted by one of ordinary skill in the artthat initial vehicle torque signal 108 may be received from flightcontroller 104 as a calculated input, user input, or combinationthereof. Flight controller 104 may include and/or communicate with anycomputing device, including without limitation a microcontroller,microprocessor, digital signal processor (DSP) and/or system on a chip(SoC). Flight controller 104 may be programmed to operate electronicaircraft to perform at least a flight maneuver; at least a flightmaneuver may include takeoff, landing, stability control maneuvers,emergency response maneuvers, regulation of altitude, roll, pitch, yaw,speed, acceleration, or the like during any phase of flight. At least aflight maneuver may include a flight plan or sequence of maneuvers to beperformed during a flight plan. Flight controller 104 may be designedand configured to operate electronic aircraft via fly-by-wire. Flightcontroller 104 is communicatively connected to each propulsor; as usedherein, flight controller 104 is communicatively connected to eachpropulsor where flight controller 104 is able to transmit signals toeach propulsor and each propulsor is configured to modify an aspect ofpropulsor behavior in response to the signals. As a non-limitingexample, flight controller 104 may transmit signals to a propulsor viaan electrical circuit connecting flight controller 104 to the propulsor;the circuit may include a direct conductive path from flight controller104 to propulsor or may include an isolated coupling such as an opticalor inductive coupling. Alternatively, or additionally, flight controller104 may communicate with a propulsor using wireless communication, suchas without limitation communication performed using electromagneticradiation including optical and/or radio communication, or communicationvia magnetic or capacitive coupling. Vehicle controller may be fullyincorporated in an electric aircraft containing a propulsor and may be aremote device operating the electric aircraft remotely via wireless orradio signals, or may be a combination thereof, such as a computingdevice in the aircraft configured to perform some steps or actionsdescribed herein while a remote device is configured to perform othersteps. Persons skilled in the art will be aware, after reviewing theentirety of this disclosure, of many different forms and protocols ofcommunication that may be used to communicatively connect flightcontroller 104 to propulsors. Persons skilled in the art, upon reviewingthe entirety of this disclosure, will be aware of various ways tomonitor resistance levels and apply resistance to linear thrust control,as used and described herein.

With continued reference to FIG. 1, system 100 includes mixer 128configured to receive initial vehicle torque signal 108 includingplurality of attitude commands 112. Receiving may include receiving oneor more electrical signals transmitted wirelessly or through a wiredconnection. Mixer 128 may be one or more computing devices configured toperform torque allocation to one or more propulsors in an electricaircraft to alter pitch, roll, yaw, and lift (or throttle). Initialvehicle torque signal 108 may be any initial vehicle torque signal asdescribed herein. Initial vehicle torque signal 108 may represent one ormore elements of data describing current, past, or future aircraftorientations relative to the earth's horizon, or attitude, thusincluding a plurality of attitude commands 112 as described herein.

With continued reference to FIG. 1, a “mixer”, for the purposes of thisdisclosure, may be a component that takes in at least an incomingsignal, such as initial vehicle torque signal 108 including plurality ofattitude commands 112 and allocates one or more outgoing signals, suchas modified attitude commands 132 and output torque command 136, or thelike, to at least a propulsor, flight component, or one or morecomputing devices connected thereto. One of ordinary skill in the art,after reading the entirety of this disclosure, would be aware that amixer, as used herein, may additionally or alternatively be described asperforming “control allocation” or “torque allocation”. For example,mixer 128 may take in commands to alter aircraft trajectory thatrequires a change in pitch and yaw. Mixer 128 may allocate torque to atleast one propulsor (or more) that do not independently alter pitch andyaw in combination to accomplish the command to change pitch and yaw.More than one propulsor may be required to adjust torques to accomplishthe command to change pitch and yaw, mixer 128 would take in the commandand allocate those torques to the appropriate propulsors consistent withthe entirety of this disclosure. One of ordinary skill in the art, afterreading the entirety of this disclosure, will appreciate the limitlesscombination of propulsors, flight components, control surfaces, orcombinations thereof that could be used in tandem to generate someamount of authority in pitch, roll, yaw, and lift of an electricaircraft consistent with this disclosure. Mixer 128 may be a nonlinearprogram-based mixer that create new frequencies from two signals appliedto it. In most applications, two signals are applied to mixer 128, andit produces new signals at the sum and difference of the originalfrequencies. Other frequency component may also be produced in apractical frequency mixer. One of ordinary skill in the art wouldunderstand that, in general, mixers are widely used to shift signalsfrom one frequency range to another, a process known as heterodyning.Another form of mixer operates by switching, with the smaller inputsignal being passed inverted or noninverted according to the phase ofthe local oscillator (LO). This would be typical of the normal operatingmode of a packaged double balanced mixer, with the local oscillatordrive considerably higher than the signal amplitude. Mixer 128 may beconsistent with any mixer described herein. Mixer 128 may be implementedusing an electrical logic circuit. “Logic circuits”, for the purposes ofthis disclosure, refer to an arrangement of electronic components suchas diodes or transistors acting as electronic switches configured to acton one or more binary inputs that produce a single binary output. Logiccircuits may include devices such as multiplexers, registers, arithmeticlogic units (ALUs), computer memory, and microprocessors, among others.In modern practice, metal-oxide-semiconductor field-effect transistors(MOSFETs) may be implemented as logic circuit components. Mixer 128 maybe implemented using a processor. Mixer 128 is configured to receive theinitial vehicle torque signal 108 for at least a propulsor from flightcontroller 104. Mixer 128 solves at least an optimization problem. Atleast an optimization problem may include solving the pitch momentfunction that may be a nonlinear program.

With continued reference to FIG. 1, system 100 includes mixer 128configured to receive at least a vehicle torque limit 116. Vehicletorque limit 116 may include one or more elements of data representingmaxima, minima, or other limits on vehicle torques, forces, attitudes,rates of change, or a combination thereof. Vehicle torque limit 116 mayinclude individual limits on one or more propulsors, one or more flightcomponents, structural stress or strain, energy consumption limits, or acombination thereof. Vehicle torque limit 116 may include attitudes inwhich aircraft cannot enter such as maximum or minimum pitch angle orpitch angle rate of change, vehicle torque limit 116 in a non-limitingexample, may include a limit on one or more propulsors calculated inorder to keep aircraft within a pitch angle range. Vehicle torque limit116 may be a relative limit, as in a non-limiting example, may includemaximum lift from one or more propulsors based on environmental factorssuch as air density. Vehicle torque limits 116 may include graphicallimits, such as points or lines on a graphical representation of certainattitudes, such as pitch vs. lift, or pitch vs. roll, for example.Vehicle torque limits 116 may be displayed to a pilot, user, or beembedded in the controls such that a pilot is unable to maneuver anaircraft that would violate a vehicle torque limit 116 as describedherein.

With continued reference to FIG. 1, mixer 128 includes circuitryconfigured to receive a plurality of prioritization data 120 including aprioritization datum 124 corresponding to each of the plurality ofattitude commands 112. Plurality of prioritization data 120 may includeone or more elements of data representing relative weight, importance,preservation, or otherwise ranking of attitudes of an aircraft.Prioritization datum 124 may be one of the plurality of prioritizationdata 120, such as the relative importance of each attitude command 112.For example, and without limitation, prioritization datum 124 mayinclude a coefficient associated with the pitch attitude command, thiscoefficient would determine a rank of preservation of pitch attitudecommand relative to roll, yaw, and lift. That is to say that if a pilotcommands aircraft to change pitch and yaw, and the command would violatevehicle torque limit 116, the mixer 128 would determine the relativelyhigher importance of pitch, and preserve the pitch command, whilecompromising the yaw command, according to available power to thepropulsor, this process will be detailed in further depth with regard toFIG. 3A and 3B.

With continued reference to FIG. 1, mixer 128 is configured to determinea plurality of modified attitude commands 132 as a function of the atleast a vehicle torque limit 116, plurality of attitude commands 112 andthe plurality of prioritization data 120. Mixer 128 may allocate torqueto plurality of propulsors such that attitude commands 112 are adjustedas a function of prioritization data 120 and vehicle torque limits 116.Modified attitude commands 132 may be prioritized to preserve moreimportant attitude commands, for instance as represented byprioritization data 120, when the vehicle torque limits 116 precludesall attitude commands 112 from being executed exactly as inputted.Modified attitude commands 132 may include one or more attitude commandswithin the vehicle torque limits 116. Mixer 128 may generate modifiedattitude command 132 for at least a propulsor as a function of solvingthe at least an optimization problem. Mixer 128 may transmit modifiedattitude command 132 to at least a propulsor. Modified attitude command132 may be used iteratively as a torque limit in a control loop suchthat system 100 can adjust at a certain rate to outside conditions suchas environmental conditions, namely airspeed, altitude, attitude, airdensity, and the like.

With continued reference to FIG. 1, mixer 128 is configured to generate,as a function of modified attitude commands 132, output torque command136. Output torque command 136 may include one or more signals to one ormore propulsors indicating the torque to be produced at the one or morepropulsors to achieve the modified attitude commands 132.

For example, and without limitation, where modified attitude command 132includes a pitch up of 5 degrees and a change in yaw to the right of 2degrees, output torque command 136 may indicate the output eachpropulsor must output individually to maneuver the aircraft in tandem.Output torque command 136 may include electrical signals consistent withthe entirety of this disclosure, which may be generated based on thetorque command in any manner that may occur to a person skilled in theart upon reviewing the entirety of this disclosure.

With continued reference to FIG. 1, mixer 128 may be configured togenerate, as a function of output torque command 136, remaining vehicletorque 140. Remaining vehicle torque 140 may include torque available ateach of a plurality of propulsors at any point during an aircraft'sentire flight envelope, such as before, during, or after a maneuver. Forexample, and without limitation, output torque command 136 may indicatestorque a propulsor must output to accomplish a maneuver; remainingvehicle torque may then be calculated based on one or more of thepropulsor limits, vehicle torque limits as described herein,environmental limits as described herein, or a combination thereof.Remaining vehicle torque 140 may be represented, as a non-limitingexample, as a total torque available at an aircraft level, such as theremaining torque available in any plane of motion or attitude componentsuch as pitch torque, roll torque, yaw torque, and/or lift torque.

With continued reference to FIG. 1, mixer 128 may be configured to solveat least an optimization problem, which may be an objective function. An“objective function,” as used in this disclosure, is a mathematicalfunction with a solution set including a plurality of data elements tobe compared. Mixer 128 may compute a score, metric, ranking, or thelike, associated with each performance prognoses and candidate transferapparatus and select objectives to minimize and/or maximize thescore/rank, depending on whether an optimal result is represented,respectively, by a minimal and/or maximal score; an objective functionmay be used by mixer 128 to score each possible pairing. At least anoptimization problem may be based on one or more objectives, asdescribed below. Mixer 128 may pair a candidate transfer apparatus, witha given combination of performance prognoses, that optimizes theobjective function. In various embodiments solving at least anoptimization problem may be based on a combination of one or morefactors. Each factor may be assigned a score based on predeterminedvariables. In some embodiments, the assigned scores may be weighted orunweighted.

Solving at least an optimization problem may include performing a greedyalgorithm process, where optimization is performed by minimizing and/ormaximizing an output of objective function. A “greedy algorithm” isdefined as an algorithm that selects locally optimal choices, which mayor may not generate a globally optimal solution. For instance, mixer 128may select objectives so that scores associated therewith are the bestscore for each goal. For instance, in non-limiting illustrative example,optimization may determine the pitch moment associated with an output ofat least a propulsor based on an input.

Still referring to FIG. 1, at least an optimization problem may beformulated as a linear objective function, which mixer 128 may optimizeusing a linear program such as without limitation a mixed-integerprogram. A “linear program,” as used in this disclosure, is a programthat optimizes a linear objective function, given at least a constraint;a linear program maybe referred to without limitation as a “linearoptimization” process and/or algorithm. For instance, in non-limitingillustrative examples, a given constraint might be torque limit, and alinear program may use a linear objective function to calculate maximumoutput based on the limit. In various embodiments, mixer 128 maydetermine a set of instructions towards achieving a user's goal thatmaximizes a total score subject to a constraint that there are othercompeting objectives. A mathematical solver may be implemented to solvefor the set of instructions that maximizes scores; mathematical solvermay be implemented on mixer 128 and/or another device in system 100,and/or may be implemented on third-party solver. At least anoptimization problem may be formulated as nonlinear least squaresoptimization process. A “nonlinear least squares optimization process,”for the purposes of this disclosure, is a form of least squares analysisused to fit a set of m observations with a model that is non-linear in nunknown parameters, where m is greater than or equal to n. The basis ofthe method is to approximate the model by a linear one and to refine theparameters by successive iterations. A nonlinear least squaresoptimization process may output a fit of signals to at least apropulsor. Solving at least an optimization problem may includeminimizing a loss function, where a “loss function” is an expression anoutput of which a ranking process minimizes to generate an optimalresult. As a non-limiting example, mixer 128 may assign variablesrelating to a set of parameters, which may correspond to scorecomponents as described above, calculate an output of mathematicalexpression using the variables, and select an objective that produces anoutput having the lowest size, according to a given definition of“size,” of the set of outputs representing each of plurality ofcandidate ingredient combinations; size may, for instance, includedabsolute value, numerical size, or the like. Selection of different lossfunctions may result in identification of different potential pairingsas generating minimal outputs.

With continued reference to FIG. 1, mixer 128 may include an inertiacompensator. An inertia compensator may include one or more computingdevices, an electrical component, circuitry, one or more logic circuitsor processors, or the like, which may configured to compensate forinertia in one or more propulsors present in system 100. Mixer 100 isconfigured, in general, to output signals and command propulsors toproduce a certain amount of torque; however, real-world propulsorscontain mass, and therefore have inertia. “Inertia”, for the purposes ofthis disclosure, is a property of matter by which it continues in itsexisting state of rest or uniform motion in a straight line, unless thatstate is changed by an external force. Specifically, in this case, amassive object requires more force or torque to start motion than isrequired to continue producing torque. In a control system, mixer 128must therefore modulate the would-be signal to account for inertia ofthe physical system being commanded. The inertia compensator may makeappropriate calculations based on modified attitude command 132, outputtorque command 136, and other considerations like environmentalconditions, available power, vehicle torque limits 116, among others.Inertia compensator may adjust vehicle torque limits 116 for certainperiods of time wherein, for example, output torque command 136 may beallowed to overspeed a propulsor to start the propulsor's rotatingphysical components and then quickly step down the torque as required tomaintain the commanded torque. The inertia compensator which may includea lead filter.

Mixer 128 may be configured to generate a first torque command for atleast a propulsor. First torque command may include at least a torquevector. First torque command may be represented in any suitable form,which may include, without limitation, vectors, matrices, coefficients,scores, ranks, or other numerical comparators, and the like. A “vector”as defined in this disclosure is a data structure that represents one ormore quantitative values and/or measures of forces, torques, signals,commands, or any other data structure as described in the entirety ofthis disclosure. A vector may be represented as an n-tuple of values,where n is at least two values, as described in further detail below; avector may alternatively or additionally be represented as an element ofa vector space, defined as a set of mathematical objects that can beadded together under an operation of addition following properties ofassociativity, commutativity, existence of an identity element, andexistence of an inverse element for each vector, and can be multipliedby scalar values under an operation of scalar multiplication compatiblewith field multiplication, and that has an identity element isdistributive with respect to vector addition, and is distributive withrespect to field addition. Each value of n-tuple of values may representa measurement or other quantitative value associated with a givencategory of data, or attribute, examples of which are provided infurther detail below; a vector may be represented, without limitation,in n-dimensional space using an axis per category of value representedin n-tuple of values, such that a vector has a geometric directioncharacterizing the relative quantities of attributes in the n-tuple ascompared to each other. Two vectors may be considered equivalent wheretheir directions, and/or the relative quantities of values within eachvector as compared to each other, are the same; thus, as a non-limitingexample, a vector represented as [5, 10, 15] may be treated asequivalent, for purposes of this disclosure, as a vector represented as[1, 2, 3]. Vectors may be more similar where their directions are moresimilar, and more different where their directions are more divergent;however, vector similarity may alternatively or additionally bedetermined using averages of similarities between like attributes, orany other measure of similarity suitable for any n-tuple of values, oraggregation of numerical similarity measures for the purposes of lossfunctions as described in further detail below. Any vectors as describedherein may be scaled, such that each vector represents each attributealong an equivalent scale of values. Each vector may be “normalized,” ordivided by a “length” attribute, such as a length attribute l as derivedusing a Pythagorean norm: l=√{square root over (Σ_(i=0) ^(n)α_(i) ²)},where α_(i) is attribute number i of the vector. Scaling and/ornormalization may function to make vector comparison independent ofabsolute quantities of attributes, while preserving any dependency onsimilarity of attributes. One of ordinary skill in the art wouldappreciate a vector to be a mathematical value consisting of a directionand magnitude.

With continued reference to FIG. 1, “torque”, for the purposes of thisdisclosure, refers to a twisting force that tends to cause rotation.Torque is the rotational equivalent of linear force. In threedimensions, the torque is a pseudovector; for point particles, it isgiven by the cross product of the position vector (distance vector) andthe force vector. The magnitude of torque of a rigid body depends onthree quantities: the force applied, the lever arm vector connecting thepoint about which the torque is being measured to the point of forceapplication, and the angle between the force and lever arm vectors. Aforce applied perpendicularly to a lever multiplied by its distance fromthe lever's fulcrum (the length of the lever arm) is its torque. A forceof three newtons applied two meters from the fulcrum, for example,exerts the same torque as a force of one newton applied six meters fromthe fulcrum. The direction of the torque can be determined by using theright-hand grip rule: if the fingers of the right hand are curled fromthe direction of the lever arm to the direction of the force, then thethumb points in the direction of the torque. One of ordinary skill inthe art would appreciate that torque is represented as a vector,consistent with this disclosure, and therefore includes a magnitude offorce and a direction. “Torque” and “moment” are equivalents for thepurposes of this disclosure. Any torque command or signal herein mayinclude at least the steady state torque to achieve the initial vehicletorque signal 108 output to at least a propulsor.

With continued reference to FIG. 1, system 100 includes display 144.Display 144 is configured to present, to a user, the remaining vehicletorque 140 and the output toque command 136. Display 144 may include agraphical user interface, multi-function display (MFD), primary display,gauges, graphs, audio cues, visual cues, information on a heads-updisplay (HUD) or a combination thereof. Display 144 may include adisplay disposed in one or more areas of an aircraft, on a user deviceremotely located, one or more computing devices, or a combinationthereof. Display 144 may be disposed in a projection, hologram, orscreen within a user's helmet, eyeglasses, contact lens, or acombination thereof. System 100 may include display 144 that displaysremaining vehicle torque 140 to a user in graphical form. Graphical formmay include a two-dimensional plot of two variables in that representreal-world data, such as pitch torque vs. roll torque of an aircraft.System 100 may include display 144 wherein the remaining vehicle torque140 is presented to a user in a graphical representation of an electricaircraft. In a non-limiting example, a graphical representation of anelectric aircraft may show arrows, levels, bar graphs, percentages, oranother representation of remaining vehicle torques in a plurality ofplanes of motion such as pitch moment, roll moment, yaw moment, and liftforce, individually or collectively. Remaining vehicle torque 140 mayinclude remaining vehicle torque capability in an aircraft's pitchmoment. Remaining vehicle torque 140 may include the remaining vehicletorque capability in an aircraft's roll moment.

Referring now to FIG. 2, mixer 128 is presented. As previouslydisclosed, solving at least an optimization problem may include solvingsequential problems relating to vehicle-level inputs to at least apropulsor, namely pitch, roll, yaw, and collective force. Mixer 128 maysolve at least an optimization problem in a specific order. An exemplarysequence is presented here in FIG. 2. According to exemplaryembodiments, mixer 128 may solve at least an optimization problemwherein the at least an optimization problem includes a pitch momentfunction 204; optimization problem may be a nonlinear program. Solvingmay be performed using a nonlinear program and/or a linear program.Mixer 128 may solve at least an optimization problem wherein solving atleast an optimization program may include solving a roll moment function208 utilizing a nonlinear program to yield the desired amount of rollmoment as a function of the desired amount of pitch moment. Mixer 128may solve at least an optimization problem wherein solving at least anoptimization program may include solving a collective force function 212utilizing a nonlinear program to yield the desired amount of collectiveforce as a function of the desired amount of pitch moment and thedesired amount of roll moment. Mixer 128 may solve at least anoptimization problem wherein solving at least an optimization programmay include solving a yaw moment function 216 utilizing a nonlinearprogram to yield the desired amount of yaw moment, as a function of thedesired amount of pitch moment, the desired amount of roll moment, andthe desired amount of collective force. One of ordinary skill in theart, after reading the entirety of this disclosure, will appreciate thatany force program may be implemented as a linear or non-linear program,as any linear program may be expressed as a nonlinear program.

Referring now to FIG. 3A, torque allocator 300 is presented in graphicalform. Torque allocator 300 may be disposed fully or partially withinmixer 128 as disclosed herein. Torque allocator 300 may include one ormore computing devices as described herein. Torque allocator 300 may bea separate component or grouping of components from those describedherein. Torque allocator 300 may be configured to allocate a portion oftotal possible torque amongst one or more propulsors based on relativepriority of a plurality attitude control commands and desired aircraftmaneuver. In a non-limiting illustrative example, torque allocationbetween two attitude control components (e.g., pitch and roll or rolland yaw) may be based on the relative priorities of those two attitudecontrol components. Priority refers to how important to the safety ofthe aircraft and any users while performing the attitude controlcomponent is relative to the other attitude control commands. Prioritymay also refer to the relative importance of each attitude controlcomponent to accomplish one or more desired aircraft maneuvers. Forexample, pitch attitude control component may be the highest priority,followed by roll, lift, and yaw attitude control components. In anotherexample, the relative priority of the attitude components may bespecific to an environment, aircraft maneuver, mission type, aircraftconfiguration, or other factors, to name a few. Torque allocator may setthe highest priority attitude control component torque allocation asclose as possible given the torque limits as described in thisdisclosure to the original command for the higher-priority attitudecontrol component, in the illustrative example, pitch, then project tothe value possible for the lower priority attitude control component, inthis case, lift. The higher priority attitude control component in thefirst torque allocation may be the attitude control component with thehighest overall priority. This process is then repeated with lowerpriority attitude control component from the above comparison and thenext highest down the priority list. In a non-limiting illustrativeexample, the next two-dimensional torque allocation problem solved wouldinclude lift and roll attitude control commands. In embodiments, thelower priority attitude command component has already been set form theprevious two-dimensional torque allocation, so this is projecting theclosest possible value for the third-level attitude command (roll inthis example). This process would repeat again for the third and fourthattitude components, in this non-limiting example, roll and yaw attitudecontrol components. Since roll is prioritized over yaw, the rollattitude control command would be preserved, and yaw would be sacrificedas a function of the vehicle torque limits as described herein. Afterthe sequence of two-dimensional attitude control component torqueallocation are completed and four prioritized attitude componentcommands are set, one or more components may send out commands to flightcontrol surfaces/propulsors to generate the set torque values allocatedin the foregoing process. As a non-limiting example of one step in thetorque allocation process, FIG. 3A illustrates a pitch axis 304 and liftaxis 308. Pitch axis 304 represents the command or plurality of attitudecommands 112 inputted to mixer 128 as described herein. Pitch axis 304may be conditioned or altered to be inputted to mixer 128. For example,and without limitation, initial vehicle torque signal 108 may includepitch and lift commands within plurality of attitude commands 112.Torque allocator 300 may also receive at least a vehicle torque limit312, which may be represented without limitation by a box plotted withinthe pitch and lift axes, which may be the same as or similar to at leasta vehicle torque limit 116. A point where pitch command and lift commandintersect may represent initial vehicle torque signal 316 as projectedonto exemplary graph of pitch and lift axes, which may be the same orsimilar to initial vehicle torque signal 108 as disclosed in theentirety of this disclosure. Torque allocator 300 utilizesprioritization data as described in the entirety of this disclosure tosolve this two-dimensional problem by preserving the higher prioritycommand and sacrificing the lower priority command. This prioritizationpreservation process may be illustrated, as a non-limiting example byplacement of modified attitude command 320, wherein the pitch commandwas preserved (horizontally translated and therefore unchanged from theinitial command), while the lift command was lessened to bring themodified attitude command within vehicle torque limits 312 (the box).Modified attitude command 328, as discussed in the entirety of thisdisclosure, may be further combined, modified, conditioned, or otherwiseadjusted to produce output torque command 136 to the plurality ofpropulsors. Remaining vehicle torque 328 represents the remaining torquecapability in one or more propulsors before, during, and after anaircraft maneuver. Remaining vehicle torque 328 may include anindividual propulsor's remaining torque capability, one or more ofpitch, roll, yaw, and lift, capabilities of one or more propulsors, theremaining vehicle-level torque or power for subsequent maneuvers.Remaining vehicle torque 328 may be displayed to a pilot or user in thegraphic presented here in FIG. 3A. The above-described is a non-limitingexample of one step in the torque allocation process. For example.

Referring now to FIG. 3B, torque allocator 300 is again presented ingraphical form of a two-dimensional plot of pitch vs. lift with inertiacompensation. Torque allocator 300 is presented in graphical form.Torque allocation process may be similar, or the same process asdescribed above in regard to FIG. 3A, with the torque limits adjustedfor inertia compensation. Torque allocator 300 may be disposed fully orpartially within mixer 128 as disclosed herein. Torque allocator 300 mayinclude one or more computing devices as described herein. Torqueallocator 300 may be a separate component or grouping of components fromthose described herein. FIG. 3 includes pitch axis 304 and lift axis308. Pitch axis 304 represents the command or plurality of attitudecommands 112 inputted to mixer 128 as described herein. Pitch axis 304may be conditioned or altered to be inputted to mixer 128. For example,and without limitation, initial vehicle torque signal 108 may includepitch and lift commands within plurality of attitude commands 112.Torque allocator 300 also receives at least a vehicle torque limit 324represented by the box plotted within the pitch and lift axes, which maybe the same as, or similar to at least a vehicle torque limit 116. Herein FIG. 3B, instead of the box being made of straight linear sides, theinertia compensation as previously discussed creates curved limits,wherein certain plurality of attitude commands 112 may be allowedwhereas without inertia compensation they would be outside of the limitsrepresented by the rectangle in FIG. 3A. Where the pitch command andlift command intersect is the initial vehicle torque signal 316, whichmay be the same or similar to initial vehicle torque signal 108 asdisclosed in the entirety of this disclosure. Torque allocator 300utilizes prioritization data as described in the entirety of thisdisclosure to solve this two-dimensional problem by preserving thehigher priority command and sacrificing the lower priority command. Thisprioritization preservation process is shown by the placement ofmodified attitude command 320, wherein the pitch command was preserved(horizontally translated and therefore unchanged from the initialcommand), while the lift command was lessened to bring the modifiedattitude command within vehicle torque limits 324 (the box). Modifiedattitude command 328 effectively commands the amount of torque to one ormore propulsors to accomplish the closest vehicle level torque toinitial vehicle torque signal 108 as possible given certain limits,maneuvers, and aircraft conditions. Modified attitude command 328, asdiscussed in the entirety of this disclosure, may be further combined,modified, conditioned, or otherwise adjusted to produce output torquecommand 136 to the plurality of propulsors. Remaining vehicle torque 328represents the remaining torque capability in one or more propulsorsbefore, during, and after an aircraft maneuver. Remaining vehicle torque328 may include an individual propulsor's remaining torque capability,one or more of pitch, roll, yaw, and lift, capabilities of one or morepropulsors, the remaining vehicle-level torque or power for subsequentmaneuvers. Remaining vehicle torque 328 may be displayed to a pilot oruser in the graphic presented here in FIG. 3A.

Now referring to FIG. 4, an exemplary embodiment 400 of a flightcontroller 404 is illustrated. As used in this disclosure a “flightcontroller” is a computing device of a plurality of computing devicesdedicated to data storage, security, distribution of traffic for loadbalancing, and flight instruction. Flight controller 404 may includeand/or communicate with any computing device as described in thisdisclosure, including without limitation a microcontroller,microprocessor, digital signal processor (DSP) and/or system on a chip(SoC) as described in this disclosure. Further, flight controller 404may include a single computing device operating independently, or mayinclude two or more computing device operating in concert, in parallel,sequentially or the like; two or more computing devices may be includedtogether in a single computing device or in two or more computingdevices. In embodiments, flight controller 404 may be installed in anaircraft, may control the aircraft remotely, and/or may include anelement installed in the aircraft and a remote element in communicationtherewith.

In an embodiment, and still referring to FIG. 4, flight controller 404may include a signal transformation component 408. As used in thisdisclosure a “signal transformation component” is a component thattransforms and/or converts a first signal to a second signal, wherein asignal may include one or more digital and/or analog signals. Forexample, and without limitation, signal transformation component 408 maybe configured to perform one or more operations such as preprocessing,lexical analysis, parsing, semantic analysis, and the like thereof. Inan embodiment, and without limitation, signal transformation component408 may include one or more analog-to-digital convertors that transforma first signal of an analog signal to a second signal of a digitalsignal. For example, and without limitation, an analog-to-digitalconverter may convert an analog input signal to a 10-bit binary digitalrepresentation of that signal. In another embodiment, signaltransformation component 408 may include transforming one or morelow-level languages such as, but not limited to, machine languagesand/or assembly languages. For example, and without limitation, signaltransformation component 408 may include transforming a binary languagesignal to an assembly language signal. In an embodiment, and withoutlimitation, signal transformation component 408 may include transformingone or more high-level languages and/or formal languages such as but notlimited to alphabets, strings, and/or languages. For example, andwithout limitation, high-level languages may include one or more systemlanguages, scripting languages, domain-specific languages, visuallanguages, esoteric languages, and the like thereof. As a furthernon-limiting example, high-level languages may include one or morealgebraic formula languages, business data languages, string and listlanguages, object-oriented languages, and the like thereof

Still referring to FIG. 4, signal transformation component 408 may beconfigured to optimize an intermediate representation 412. As used inthis disclosure an “intermediate representation” is a data structureand/or code that represents the input signal. Signal transformationcomponent 408 may optimize intermediate representation as a function ofa data-flow analysis, dependence analysis, alias analysis, pointeranalysis, escape analysis, and the like thereof. In an embodiment, andwithout limitation, signal transformation component 408 may optimizeintermediate representation 412 as a function of one or more inlineexpansions, dead code eliminations, constant propagation, looptransformations, and/or automatic parallelization functions. In anotherembodiment, signal transformation component 408 may optimizeintermediate representation as a function of a machine dependentoptimization such as a peephole optimization, wherein a peepholeoptimization may rewrite short sequences of code into more efficientsequences of code. Signal transformation component 408 may optimizeintermediate representation to generate an output language, wherein an“output language,” as used herein, is the native machine language offlight controller 404. For example, and without limitation, nativemachine language may include one or more binary and/or numericallanguages.

In an embodiment, and without limitation, signal transformationcomponent 408 may include transform one or more inputs and outputs as afunction of an error correction code. An error correction code, alsoknown as error correcting code (ECC), is an encoding of a message or lotof data using redundant information, permitting recovery of corrupteddata. An ECC may include a block code, in which information is encodedon fixed-size packets and/or blocks of data elements such as symbols ofpredetermined size, bits, or the like. Reed-Solomon coding, in whichmessage symbols within a symbol set having q symbols are encoded ascoefficients of a polynomial of degree less than or equal to a naturalnumber k, over a finite field F with q elements; strings so encoded havea minimum hamming distance of k+1, and permit correction of (q−k−1)/2erroneous symbols. Block code may alternatively or additionally beimplemented using Golay coding, also known as binary Golay coding,Bose-Chaudhuri, Hocquenghuem (BCH) coding, multidimensional parity-checkcoding, and/or Hamming codes. An ECC may alternatively or additionallybe based on a convolutional code.

In an embodiment, and still referring to FIG. 4, flight controller 404may include a reconfigurable hardware platform 416. A “reconfigurablehardware platform,” as used herein, is a component and/or unit ofhardware that may be reprogrammed, such that, for instance, a data pathbetween elements such as logic gates or other digital circuit elementsmay be modified to change an algorithm, state, logical sequence, or thelike of the component and/or unit. This may be accomplished with suchflexible high-speed computing fabrics as field-programmable gate arrays(FPGAs), which may include a grid of interconnected logic gates,connections between which may be severed and/or restored to program inmodified logic. Reconfigurable hardware platform 416 may be reconfiguredto enact any algorithm and/or algorithm selection process received fromanother computing device and/or created using machine-learningprocesses.

Still referring to FIG. 4, reconfigurable hardware platform 416 mayinclude a logic component 420. As used in this disclosure a “logiccomponent” is a component that executes instructions on output language.For example, and without limitation, logic component may perform basicarithmetic, logic, controlling, input/output operations, and the likethereof. Logic component 420 may include any suitable processor, such aswithout limitation a component incorporating logical circuitry forperforming arithmetic and logical operations, such as an arithmetic andlogic unit (ALU), which may be regulated with a state machine anddirected by operational inputs from memory and/or sensors; logiccomponent 420 may be organized according to Von Neumann and/or Harvardarchitecture as a non-limiting example. Logic component 420 may include,incorporate, and/or be incorporated in, without limitation, amicrocontroller, microprocessor, digital signal processor (DSP), FieldProgrammable Gate Array (FPGA), Complex Programmable Logic Device(CPLD), Graphical Processing Unit (GPU), general purpose GPU, TensorProcessing Unit (TPU), analog or mixed signal processor, TrustedPlatform Module (TPM), a floating point unit (FPU), and/or system on achip (SoC). In an embodiment, logic component 420 may include one ormore integrated circuit microprocessors, which may contain one or morecentral processing units, central processors, and/or main processors, ona single metal-oxide-semiconductor chip. Logic component 420 may beconfigured to execute a sequence of stored instructions to be performedon the output language and/or intermediate representation 412. Logiccomponent 420 may be configured to fetch and/or retrieve the instructionfrom a memory cache, wherein a “memory cache,” as used in thisdisclosure, is a stored instruction set on flight controller 404. Logiccomponent 420 may be configured to decode the instruction retrieved fromthe memory cache to opcodes and/or operands. Logic component 420 may beconfigured to execute the instruction on intermediate representation 412and/or output language. For example, and without limitation, logiccomponent 420 may be configured to execute an addition operation onintermediate representation 412 and/or output language.

In an embodiment, and without limitation, logic component 420 may beconfigured to calculate a flight element 424. As used in this disclosurea “flight element” is an element of datum denoting a relative status ofaircraft. For example, and without limitation, flight element 424 maydenote one or more torques, thrusts, airspeed velocities, forces,altitudes, groundspeed velocities, directions during flight, directionsfacing, forces, orientations, and the like thereof. For example, andwithout limitation, flight element 424 may denote that aircraft iscruising at an altitude and/or with a sufficient magnitude of forwardthrust. As a further non-limiting example, flight status may denote thatis building thrust and/or groundspeed velocity in preparation for atakeoff. As a further non-limiting example, flight element 424 maydenote that aircraft is following a flight path accurately and/orsufficiently.

Still referring to FIG. 4, flight controller 404 may include a chipsetcomponent 428. As used in this disclosure a “chipset component” is acomponent that manages data flow. In an embodiment, and withoutlimitation, chipset component 428 may include a northbridge data flowpath, wherein the northbridge dataflow path may manage data flow fromlogic component 420 to a high-speed device and/or component, such as aRAM, graphics controller, and the like thereof. In another embodiment,and without limitation, chipset component 428 may include a southbridgedata flow path, wherein the southbridge dataflow path may manage dataflow from logic component 420 to lower-speed peripheral buses, such as aperipheral component interconnect (PCI), industry standard architecture(ICA), and the like thereof. In an embodiment, and without limitation,southbridge data flow path may include managing data flow betweenperipheral connections such as ethernet, USB, audio devices, and thelike thereof. Additionally or alternatively, chipset component 428 maymanage data flow between logic component 420, memory cache, and a flightcomponent 432. As used in this disclosure a “flight component” is aportion of an aircraft that can be moved or adjusted to affect one ormore flight elements. For example, flight component432 may include acomponent used to affect the aircrafts' roll and pitch which maycomprise one or more ailerons. As a further example, flight component432 may include a rudder to control yaw of an aircraft. In anembodiment, chipset component 428 may be configured to communicate witha plurality of flight components as a function of flight element 424.For example, and without limitation, chipset component 428 may transmitto an aircraft rotor to reduce torque of a first lift propulsor andincrease the forward thrust produced by a pusher component to perform aflight maneuver.

In an embodiment, and still referring to FIG. 4, flight controller 404may be configured generate an autonomous function. As used in thisdisclosure an “autonomous function” is a mode and/or function of flightcontroller 404 that controls aircraft automatically. For example, andwithout limitation, autonomous function may perform one or more aircraftmaneuvers, take offs, landings, altitude adjustments, flight levelingadjustments, turns, climbs, and/or descents. As a further non-limitingexample, autonomous function may adjust one or more airspeed velocities,thrusts, torques, and/or groundspeed velocities. As a furthernon-limiting example, autonomous function may perform one or more flightpath corrections and/or flight path modifications as a function offlight element 424. In an embodiment, autonomous function may includeone or more modes of autonomy such as, but not limited to, autonomousmode, semi-autonomous mode, and/or non-autonomous mode. As used in thisdisclosure “autonomous mode” is a mode that automatically adjusts and/orcontrols aircraft and/or the maneuvers of aircraft in its entirety. Forexample, autonomous mode may denote that flight controller 404 willadjust the aircraft. As used in this disclosure a “semi-autonomous mode”is a mode that automatically adjusts and/or controls a portion and/orsection of aircraft. For example, and without limitation,semi-autonomous mode may denote that a pilot will control thepropulsors, wherein flight controller 404 will control the aileronsand/or rudders. In another non-limiting example semi-autonomous mode maypermit a pilot's operation within an acceptable control range, such thatif pilot controls input values outside of the acceptable control rangeflight controller will operate independent (autonomously) from the pilotcontrols. As used in this disclosure “non-autonomous mode” is a modethat denotes a pilot will control aircraft and/or maneuvers of aircraftin its entirety.

In some embodiments, and still referring to FIG. 4, semi-autonomous modemay include an autonomous mode that is overridable by a user, forexample a pilot. For example in some embodiments, flight controller 404may operate in a semi-autonomous mode wherein the aircraft is fullycontrolled in an autonomous mode, but also is responsive to pilotcontrol inputs as received. In another embodiment, flight controller mayoperate in a semi-autonomous mode wherein pilot is displayed orotherwise communicated a range of controls within which flightcontroller deems an acceptable control range and pilot remains incomplete control of aircraft, but through communication of theacceptable control range the flight controller 404 may be said to beoperating within a semi-autonomous mode.

In an embodiment, and still referring to FIG. 4, flight controller 404may generate autonomous function as a function of an autonomousmachine-learning model. As used in this disclosure an “autonomousmachine-learning model” is a machine-learning model to produce anautonomous function output given flight element 424 and a pilot signal436 as inputs; this is in contrast to a non-machine learning softwareprogram where the commands to be executed are determined in advance by auser and written in a programming language. As used in this disclosure a“pilot signal” is an element of datum representing one or more functionsa pilot is controlling and/or adjusting. For example, pilot signal 436may denote that a pilot is controlling and/or maneuvering ailerons,wherein the pilot is not in control of the rudders and/or propulsors. Inan embodiment, pilot signal 436 may include an implicit signal and/or anexplicit signal. For example, and without limitation, pilot signal 436may include an explicit signal, wherein the pilot explicitly statesthere is a lack of control and/or desire for autonomous function. As afurther non-limiting example, pilot signal 436 may include an explicitsignal directing flight controller 404 to control and/or maintain aportion of aircraft, a portion of the flight plan, the entire aircraft,and/or the entire flight plan. As a further non-limiting example, pilotsignal 436 may include an implicit signal, wherein flight controller 404detects a lack of control such as by a malfunction, torque alteration,flight path deviation, and the like thereof. In an embodiment, andwithout limitation, pilot signal 436 may include one or more explicitsignals to reduce torque, and/or one or more implicit signals thattorque may be reduced due to reduction of airspeed velocity. In anembodiment, and without limitation, pilot signal 436 may include one ormore local and/or global signals. For example, and without limitation,pilot signal 436 may include a local signal that is transmitted by apilot and/or crew member. As a further non-limiting example, pilotsignal 436 may include a global signal that is transmitted by airtraffic control and/or one or more remote users that are incommunication with the pilot of aircraft. In an embodiment, pilot signal436 may be received as a function of a tri-state bus and/or multiplexorthat denotes an explicit pilot signal should be transmitted prior to anyimplicit or global pilot signal.

Still referring to FIG. 4, autonomous machine-learning model may includeone or more autonomous machine-learning processes such as supervised,unsupervised, or reinforcement machine-learning processes that flightcontroller 404 and/or a remote device may or may not use in thegeneration of autonomous function. As used in this disclosure “remotedevice” is an external device to flight controller 404. Additionally oralternatively, autonomous machine-learning model may include one or moreautonomous machine-learning processes that a field-programmable gatearray (FPGA) may or may not use in the generation of autonomousfunction. Autonomous machine-learning process may include, withoutlimitation machine learning processes such as simple linear regression,multiple linear regression, polynomial regression, support vectorregression, ridge regression, lasso regression, elasticnet regression,decision tree regression, random forest regression, logistic regression,logistic classification, K-nearest neighbors, support vector machines,kernel support vector machines, naïve bayes, decision treeclassification, random forest classification, K-means clustering,hierarchical clustering, dimensionality reduction, principal componentanalysis, linear discriminant analysis, kernel principal componentanalysis, Q-learning, State Action Reward State Action (SARSA), Deep-Qnetwork, Markov decision processes, Deep Deterministic Policy Gradient(DDPG), or the like thereof.

In an embodiment, and still referring to FIG. 4, autonomous machinelearning model may be trained as a function of autonomous training data,wherein autonomous training data may correlate a flight element, pilotsignal, and/or simulation data to an autonomous function. For example,and without limitation, a flight element of an airspeed velocity, apilot signal of limited and/or no control of propulsors, and asimulation data of required airspeed velocity to reach the destinationmay result in an autonomous function that includes a semi-autonomousmode to increase thrust of the propulsors. Autonomous training data maybe received as a function of user-entered valuations of flight elements,pilot signals, simulation data, and/or autonomous functions. Flightcontroller 404 may receive autonomous training data by receivingcorrelations of flight element, pilot signal, and/or simulation data toan autonomous function that were previously received and/or determinedduring a previous iteration of generation of autonomous function.Autonomous training data may be received by one or more remote devicesand/or FPGAs that at least correlate a flight element, pilot signal,and/or simulation data to an autonomous function. Autonomous trainingdata may be received in the form of one or more user-enteredcorrelations of a flight element, pilot signal, and/or simulation datato an autonomous function.

Still referring to FIG. 4, flight controller 404 may receive autonomousmachine-learning model from a remote device and/or FPGA that utilizesone or more autonomous machine learning processes, wherein a remotedevice and an FPGA is described above in detail. For example, andwithout limitation, a remote device may include a computing device,external device, processor, FPGA, microprocessor and the like thereof.Remote device and/or FPGA may perform the autonomous machine-learningprocess using autonomous training data to generate autonomous functionand transmit the output to flight controller 404. Remote device and/orFPGA may transmit a signal, bit, datum, or parameter to flightcontroller 404 that at least relates to autonomous function.Additionally or alternatively, the remote device and/or FPGA may providean updated machine-learning model. For example, and without limitation,an updated machine-learning model may be comprised of a firmware update,a software update, a autonomous machine-learning process correction, andthe like thereof. As a non-limiting example a software update mayincorporate a new simulation data that relates to a modified flightelement. Additionally or alternatively, the updated machine learningmodel may be transmitted to the remote device and/or FPGA, wherein theremote device and/or FPGA may replace the autonomous machine-learningmodel with the updated machine-learning model and generate theautonomous function as a function of the flight element, pilot signal,and/or simulation data using the updated machine-learning model. Theupdated machine-learning model may be transmitted by the remote deviceand/or FPGA and received by flight controller 404 as a software update,firmware update, or corrected habit machine-learning model. For example,and without limitation autonomous machine learning model may utilize aneural net machine-learning process, wherein the updatedmachine-learning model may incorporate a gradient boostingmachine-learning process.

Still referring to FIG. 4, flight controller 404 may include, beincluded in, and/or communicate with a mobile device such as a mobiletelephone or smartphone. Further, flight controller may communicate withone or more additional devices as described below in further detail viaa network interface device. The network interface device may be utilizedfor commutatively connecting a flight controller to one or more of avariety of networks, and one or more devices. Examples of a networkinterface device include, but are not limited to, a network interfacecard (e.g., a mobile network interface card, a LAN card), a modem, andany combination thereof. Examples of a network include, but are notlimited to, a wide area network (e.g., the Internet, an enterprisenetwork), a local area network (e.g., a network associated with anoffice, a building, a campus or other relatively small geographicspace), a telephone network, a data network associated with atelephone/voice provider (e.g., a mobile communications provider dataand/or voice network), a direct connection between two computingdevices, and any combinations thereof. The network may include anynetwork topology and can may employ a wired and/or a wireless mode ofcommunication.

In an embodiment, and still referring to FIG. 4, flight controller 404may include, but is not limited to, for example, a cluster of flightcontrollers in a first location and a second flight controller orcluster of flight controllers in a second location. Flight controller404 may include one or more flight controllers dedicated to datastorage, security, distribution of traffic for load balancing, and thelike. Flight controller 404 may be configured to distribute one or morecomputing tasks as described below across a plurality of flightcontrollers, which may operate in parallel, in series, redundantly, orin any other manner used for distribution of tasks or memory betweencomputing devices. For example, and without limitation, flightcontroller 404 may implement a control algorithm to distribute and/orcommand the plurality of flight controllers. As used in this disclosurea “control algorithm” is a finite sequence of well-defined computerimplementable instructions that may determine the flight component ofthe plurality of flight components to be adjusted. For example, andwithout limitation, control algorithm may include one or more algorithmsthat reduce and/or prevent aviation asymmetry. As a further non-limitingexample, control algorithms may include one or more models generated asa function of a software including, but not limited to Simulink byMathWorks, Natick, Mass., USA. In an embodiment, and without limitation,control algorithm may be configured to generate an auto-code, wherein an“auto-code,” is used herein, is a code and/or algorithm that isgenerated as a function of the one or more models and/or software's. Inanother embodiment, control algorithm may be configured to produce asegmented control algorithm. As used in this disclosure a “segmentedcontrol algorithm” is control algorithm that has been separated and/orparsed into discrete sections. For example, and without limitation,segmented control algorithm may parse control algorithm into two or moresegments, wherein each segment of control algorithm may be performed byone or more flight controllers operating on distinct flight components.

In an embodiment, and still referring to FIG. 4, control algorithm maybe configured to determine a segmentation boundary as a function ofsegmented control algorithm. As used in this disclosure a “segmentationboundary” is a limit and/or delineation associated with the segments ofthe segmented control algorithm. For example, and without limitation,segmentation boundary may denote that a segment in the control algorithmhas a first starting section and/or a first ending section. As a furthernon-limiting example, segmentation boundary may include one or moreboundaries associated with an ability of flight component 432. In anembodiment, control algorithm may be configured to create an optimizedsignal communication as a function of segmentation boundary. Forexample, and without limitation, optimized signal communication mayinclude identifying the discrete timing required to transmit and/orreceive the one or more segmentation boundaries. In an embodiment, andwithout limitation, creating optimized signal communication furthercomprises separating a plurality of signal codes across the plurality offlight controllers. For example, and without limitation the plurality offlight controllers may include one or more formal networks, whereinformal networks transmit data along an authority chain and/or arelimited to task-related communications. As a further non-limitingexample, communication network may include informal networks, whereininformal networks transmit data in any direction. In an embodiment, andwithout limitation, the plurality of flight controllers may include achain path, wherein a “chain path,” as used herein, is a linearcommunication path comprising a hierarchy that data may flow through. Inan embodiment, and without limitation, the plurality of flightcontrollers may include an all-channel path, wherein an “all-channelpath,” as used herein, is a communication path that is not restricted toa particular direction. For example, and without limitation, data may betransmitted upward, downward, laterally, and the like thereof. In anembodiment, and without limitation, the plurality of flight controllersmay include one or more neural networks that assign a weighted value toa transmitted datum. For example, and without limitation, a weightedvalue may be assigned as a function of one or more signals denoting thata flight component is malfunctioning and/or in a failure state.

Still referring to FIG. 4, the plurality of flight controllers mayinclude a master bus controller. As used in this disclosure a “masterbus controller” is one or more devices and/or components that areconnected to a bus to initiate a direct memory access transaction,wherein a bus is one or more terminals in a bus architecture. Master buscontroller may communicate using synchronous and/or asynchronous buscontrol protocols. In an embodiment, master bus controller may includeflight controller 404. In another embodiment, master bus controller mayinclude one or more universal asynchronous receiver-transmitters (UART).For example, and without limitation, master bus controller may includeone or more bus architectures that allow a bus to initiate a directmemory access transaction from one or more buses in the busarchitectures. As a further non-limiting example, master bus controllermay include one or more peripheral devices and/or components tocommunicate with another peripheral device and/or component and/or themaster bus controller. In an embodiment, master bus controller may beconfigured to perform bus arbitration. As used in this disclosure “busarbitration” is method and/or scheme to prevent multiple buses fromattempting to communicate with and/or connect to master bus controller.For example and without limitation, bus arbitration may include one ormore schemes such as a small computer interface system, wherein a smallcomputer interface system is a set of standards for physical connectingand transferring data between peripheral devices and master buscontroller by defining commands, protocols, electrical, optical, and/orlogical interfaces. In an embodiment, master bus controller may receiveintermediate representation 412 and/or output language from logiccomponent 420, wherein output language may include one or moreanalog-to-digital conversions, low bit rate transmissions, messageencryptions, digital signals, binary signals, logic signals, analogsignals, and the like thereof described above in detail.

Still referring to FIG. 4, master bus controller may communicate with aslave bus. As used in this disclosure a “slave bus” is one or moreperipheral devices and/or components that initiate a bus transfer. Forexample, and without limitation, slave bus may receive one or morecontrols and/or asymmetric communications from master bus controller,wherein slave bus transfers data stored to master bus controller. In anembodiment, and without limitation, slave bus may include one or moreinternal buses, such as but not limited to a/an internal data bus,memory bus, system bus, front-side bus, and the like thereof. In anotherembodiment, and without limitation, slave bus may include one or moreexternal buses such as external flight controllers, external computers,remote devices, printers, aircraft computer systems, flight controlsystems, and the like thereof.

In an embodiment, and still referring to FIG. 4, control algorithm mayoptimize signal communication as a function of determining one or morediscrete timings. For example, and without limitation master buscontroller may synchronize timing of the segmented control algorithm byinjecting high priority timing signals on a bus of the master buscontrol. As used in this disclosure a “high priority timing signal” isinformation denoting that the information is important. For example, andwithout limitation, high priority timing signal may denote that asection of control algorithm is of high priority and should be analyzedand/or transmitted prior to any other sections being analyzed and/ortransmitted. In an embodiment, high priority timing signal may includeone or more priority packets. As used in this disclosure a “prioritypacket” is a formatted unit of data that is communicated between theplurality of flight controllers. For example, and without limitation,priority packet may denote that a section of control algorithm should beused and/or is of greater priority than other sections.

Still referring to FIG. 4, flight controller 404 may also be implementedusing a “shared nothing” architecture in which data is cached at theworker, in an embodiment, this may enable scalability of aircraft and/orcomputing device. Flight controller 404 may include a distributer flightcontroller. As used in this disclosure a “distributer flight controller”is a component that adjusts and/or controls a plurality of flightcomponents as a function of a plurality of flight controllers. Forexample, distributer flight controller may include a flight controllerthat communicates with a plurality of additional flight controllersand/or clusters of flight controllers. In an embodiment, distributedflight control may include one or more neural networks. For example,neural network also known as an artificial neural network, is a networkof “nodes,” or data structures having one or more inputs, one or moreoutputs, and a function determining outputs based on inputs. Such nodesmay be organized in a network, such as without limitation aconvolutional neural network, including an input layer of nodes, one ormore intermediate layers, and an output layer of nodes. Connectionsbetween nodes may be created via the process of “training” the network,in which elements from a training dataset are applied to the inputnodes, a suitable training algorithm (such as Levenberg-Marquardt,conjugate gradient, simulated annealing, or other algorithms) is thenused to adjust the connections and weights between nodes in adjacentlayers of the neural network to produce the desired values at the outputnodes. This process is sometimes referred to as deep learning.

Still referring to FIG. 4, a node may include, without limitation aplurality of inputs x_(i) that may receive numerical values from inputsto a neural network containing the node and/or from other nodes. Nodemay perform a weighted sum of inputs using weights w_(i) that aremultiplied by respective inputs x_(i). Additionally or alternatively, abias b may be added to the weighted sum of the inputs such that anoffset is added to each unit in the neural network layer that isindependent of the input to the layer. The weighted sum may then beinput into a function φ, which may generate one or more outputs y.Weight w_(i) applied to an input x_(i) may indicate whether the input is“excitatory,” indicating that it has strong influence on the one or moreoutputs y, for instance by the corresponding weight having a largenumerical value, and/or a “inhibitory,” indicating it has a weak effectinfluence on the one more inputs y, for instance by the correspondingweight having a small numerical value. The values of weights w_(i) maybe determined by training a neural network using training data, whichmay be performed using any suitable process as described above. In anembodiment, and without limitation, a neural network may receivesemantic units as inputs and output vectors representing such semanticunits according to weights w_(i) that are derived using machine-learningprocesses as described in this disclosure.

Still referring to FIG. 4, flight controller may include asub-controller 440. As used in this disclosure a “sub-controller” is acontroller and/or component that is part of a distributed controller asdescribed above; for instance, flight controller 404 may be and/orinclude a distributed flight controller made up of one or moresub-controllers. For example, and without limitation, sub-controller 440may include any controllers and/or components thereof that are similarto distributed flight controller and/or flight controller as describedabove. Sub-controller 440 may include any component of any flightcontroller as described above. Sub-controller 440 may be implemented inany manner suitable for implementation of a flight controller asdescribed above. As a further non-limiting example, sub-controller 440may include one or more processors, logic components and/or computingdevices capable of receiving, processing, and/or transmitting dataacross the distributed flight controller as described above. As afurther non-limiting example, sub-controller 440 may include acontroller that receives a signal from a first flight controller and/orfirst distributed flight controller component and transmits the signalto a plurality of additional sub-controllers and/or flight components.

Still referring to FIG. 4, flight controller may include a co-controller444. As used in this disclosure a “co-controller” is a controller and/orcomponent that joins flight controller 404 as components and/or nodes ofa distributer flight controller as described above. For example, andwithout limitation, co-controller 444 may include one or morecontrollers and/or components that are similar to flight controller 404.As a further non-limiting example, co-controller 444 may include anycontroller and/or component that joins flight controller 404 todistributer flight controller. As a further non-limiting example,co-controller 444 may include one or more processors, logic componentsand/or computing devices capable of receiving, processing, and/ortransmitting data to and/or from flight controller 404 to distributedflight control system. Co-controller 444 may include any component ofany flight controller as described above. Co-controller 444 may beimplemented in any manner suitable for implementation of a flightcontroller as described above.

In an embodiment, and with continued reference to FIG. 4, flightcontroller 404 may be designed and/or configured to perform any method,method step, or sequence of method steps in any embodiment described inthis disclosure, in any order and with any degree of repetition. Forinstance, flight controller 404 may be configured to perform a singlestep or sequence repeatedly until a desired or commanded outcome isachieved; repetition of a step or a sequence of steps may be performediteratively and/or recursively using outputs of previous repetitions asinputs to subsequent repetitions, aggregating inputs and/or outputs ofrepetitions to produce an aggregate result, reduction or decrement ofone or more variables such as global variables, and/or division of alarger processing task into a set of iteratively addressed smallerprocessing tasks. Flight controller may perform any step or sequence ofsteps as described in this disclosure in parallel, such assimultaneously and/or substantially simultaneously performing a step twoor more times using two or more parallel threads, processor cores, orthe like; division of tasks between parallel threads and/or processesmay be performed according to any protocol suitable for division oftasks between iterations. Persons skilled in the art, upon reviewing theentirety of this disclosure, will be aware of various ways in whichsteps, sequences of steps, processing tasks, and/or data may besubdivided, shared, or otherwise dealt with using iteration, recursion,and/or parallel processing.

Referring now to FIG. 5, a method 500 is presented in flow diagram formfor flight control configured for use in an electric aircraft. At 505,method 500 includes providing, at the flight controller, an initialvehicle torque signal including a plurality of attitude commands. Theflight controller may be any flight controller as described herein. Theinitial vehicle torque signal may be any initial vehicle torque signalas described herein. The plurality of attitude commands may include anyattitude commands as described herein. The flight controller may includea proportional-integral-derivative (PID) controller. In someembodiments, providing initial vehicle torque signal may additionallyinclude providing, using flight controller, the initial vehicle torquesignal as a function of a control algorithm. In some embodiments,providing initial vehicle torque signal may additionally includeproviding, using flight controller, the initial vehicle torque signal asa function of an autonomous function.

At 510, method 500 includes receiving, at the mixer, the initial vehicletorque signal including a plurality of attitude commands. The mixer maybe any mixer as described herein. The mixer may include an inertiacompensator as described herein. The inertia compensator may include alead filter, as described herein. The mixer may include an electricallogic circuit as described herein. The mixer may include a processor asdescribed herein. The initial vehicle torque signal may include anyinitial vehicle torque signal as described herein.

At 515, method 500 includes receiving, at the mixer, at least a vehicletorque limit. The at least a vehicle torque limit may include anyvehicle torque limit as described herein, including but not limited toindividual propulsor limits, vehicle-level attitude limits, collectivepropulsor torque limits, and collective torque lift force limits, amongothers.

At 520, method 500 includes receiving a plurality of prioritization dataincluding a prioritization datum corresponding to the plurality ofattitude commands. The prioritization data may be any prioritizationdescribed herein. The prioritization datum may include anyprioritization datum as described herein. The plurality of attitudecommands may include any of the plurality of attitude commands asdescribed herein.

At 525, method 500 includes determining modified attitude commands as afunction of the at least a vehicle torque limit, plurality of attitudecommands, and prioritization data. The at least a vehicle torque limitmay be any vehicle torque limit as described herein. The plurality ofattitude commands may include any of the plurality of attitude commandsas described herein. The prioritization data may include anyprioritization data as described herein.

At 530, method 500 includes generating, at the mixer, an output torquecommand as a function of the modified torque commands. The output torquecommand may include any output torque command as described herein. Themodified torque commands may include any modified torque commands asdescribed herein.

At 535, method 500 includes generating, at the mixer, a remainingvehicle torque as a function of the output torque command. Remainingvehicle torque may include any remaining vehicle torque as describedherein. The output torque command may include any output torque commandas described herein. The remaining vehicle torque may include theremaining vehicle torque capability in an aircraft's yaw moment. Theremaining vehicle torque may include the remaining vehicle torquecapability in an aircraft's assisted lift.

Any of the herein disclosed system and methods may be implemented usingmachine-learning. Referring now to FIG. 6, “training data,” as usedherein, is data containing correlations that a machine-learning processmay use to model relationships between two or more categories of dataelements. For instance, and without limitation, training data 604 mayinclude a plurality of data entries, each entry representing a set ofdata elements that were recorded, received, and/or generated together;data elements may be correlated by shared existence in a given dataentry, by proximity in a given data entry, or the like. Multiple dataentries in training data 604 may evince one or more trends incorrelations between categories of data elements; for instance, andwithout limitation, a higher value of a first data element belonging toa first category of data element may tend to correlate to a higher valueof a second data element belonging to a second category of data element,indicating a possible proportional or other mathematical relationshiplinking values belonging to the two categories. Multiple categories ofdata elements may be related in training data 604 according to variouscorrelations; correlations may indicate causative and/or predictivelinks between categories of data elements, which may be modeled asrelationships such as mathematical relationships by machine-learningprocesses as described in further detail below. Training data 604 may beformatted and/or organized by categories of data elements, for instanceby associating data elements with one or more descriptors correspondingto categories of data elements. As a non-limiting example, training data604 may include data entered in standardized forms by persons orprocesses, such that entry of a given data element in a given field in aform may be mapped to one or more descriptors of categories. Elements intraining data 604 may be linked to descriptors of categories by tags,tokens, or other data elements; for instance, and without limitation,training data 604 may be provided in fixed-length formats, formatslinking positions of data to categories such as comma-separated value(CSV) formats and/or self-describing formats such as extensible markuplanguage (XML), JavaScript Object Notation (JSON), or the like, enablingprocesses or devices to detect categories of data.

Alternatively or additionally, and continuing to refer to FIG. 6,training data 604 may include one or more elements that are notcategorized; that is, training data 604 may not be formatted or containdescriptors for some elements of data. Machine-learning algorithmsand/or other processes may sort training data 604 according to one ormore categorizations using, for instance, natural language processingalgorithms, tokenization, detection of correlated values in raw data andthe like; categories may be generated using correlation and/or otherprocessing algorithms. As a non-limiting example, in a corpus of text,phrases making up a number “n” of compound words, such as nouns modifiedby other nouns, may be identified according to a statisticallysignificant prevalence of n-grams containing such words in a particularorder; such an n-gram may be categorized as an element of language suchas a “word” to be tracked similarly to single words, generating a newcategory as a result of statistical analysis. Similarly, in a data entryincluding some textual data, a person's name may be identified byreference to a list, dictionary, or other compendium of terms,permitting ad-hoc categorization by machine-learning algorithms, and/orautomated association of data in the data entry with descriptors or intoa given format. The ability to categorize data entries automatedly mayenable the same training data 604 to be made applicable for two or moredistinct machine-learning algorithms as described in further detailbelow. Training data 604 used by machine-learning module 600 maycorrelate any input data as described in this disclosure to any outputdata as described in this disclosure. As a non-limiting illustrativeexample machine-learning process may input user preferences andcandidate transfer apparatus performance archive 124 and output aranking of performance prognoses.

Further referring to FIG. 6, training data may be filtered, sorted,and/or selected using one or more supervised and/or unsupervisedmachine-learning processes and/or models as described in further detailbelow; such models may include without limitation a training dataclassifier 616. Training data classifier 616 may include a “classifier,”which as used in this disclosure is a machine-learning model as definedbelow, such as a mathematical model, neural net, or program generated bya machine-learning algorithm known as a “classification algorithm,” asdescribed in further detail below, that sorts inputs into categories orbins of data, outputting the categories or bins of data and/or labelsassociated therewith. A classifier may be configured to output at leasta datum that labels or otherwise identifies a set of data that areclustered together, found to be close under a distance metric asdescribed below, or the like. Machine-learning module 600 may generate aclassifier using a classification algorithm, defined as processeswhereby a computing device and/or any module and/or component operatingthereon derives a classifier from training data 604. Classification maybe performed using, without limitation, linear classifiers such aswithout limitation logistic regression and/or naive Bayes classifiers,nearest neighbor classifiers such as k-nearest neighbors classifiers,support vector machines, least squares support vector machines, fisher'slinear discriminant, quadratic classifiers, decision trees, boostedtrees, random forest classifiers, learning vector quantization, and/orneural network-based classifiers. As a non-limiting example, trainingdata classifier 616 may classify elements of training data to onlycorrelate performance prognoses to candidate transfer apparatuses fortransfer apparatuses capable of completing a transfer invocation,similarly to a cohort of persons and/or other analyzed items and/orphenomena for which a subset of training data may be selected.

Still referring to FIG. 6, machine-learning module 600 may be configuredto perform a lazy-learning process 620 and/or protocol, which mayalternatively be referred to as a “lazy loading” or “call-when-needed”process and/or protocol, may be a process whereby machine learning isconducted upon receipt of an input to be converted to an output, bycombining the input and training set to derive the algorithm to be usedto produce the output on demand. For instance, an initial set ofsimulations may be performed to cover an initial heuristic and/or “firstguess” at an output and/or relationship. As a non-limiting example, aninitial heuristic may include a ranking of associations between inputsand elements of training data 604. Heuristic may include selecting somenumber of highest-ranking associations and/or training data 604elements. Lazy learning may implement any suitable lazy learningalgorithm, including without limitation a K-nearest neighbors algorithm,a lazy naive Bayes algorithm, or the like; persons skilled in the art,upon reviewing the entirety of this disclosure, will be aware of variouslazy-learning algorithms that may be applied to generate outputs asdescribed in this disclosure, including without limitation lazy learningapplications of machine-learning algorithms as described in furtherdetail below.

Alternatively, or additionally, and with continued reference to FIG. 6,machine-learning processes as described in this disclosure may be usedto generate machine-learning models 624. A “machine-learning model,” asused in this disclosure, is a mathematical and/or algorithmicrepresentation of a relationship between inputs and outputs, asgenerated using any machine-learning process including withoutlimitation any process as described above and stored in memory; an inputis submitted to a machine-learning model 624 once created, whichgenerates an output based on the relationship that was derived. Forinstance, and without limitation, a linear regression model, generatedusing a linear regression algorithm, may compute a linear combination ofinput data using coefficients derived during machine-learning processesto calculate an output datum. As a further non-limiting example, amachine-learning model 624 may be generated by creating an artificialneural network, such as a convolutional neural network comprising aninput layer of nodes, one or more intermediate layers, and an outputlayer of nodes. Connections between nodes may be created via the processof “training” the network, in which elements from a training data 604set are applied to the input nodes, a suitable training algorithm (suchas Levenberg-Marquardt, conjugate gradient, simulated annealing, orother algorithms) is then used to adjust the connections and weightsbetween nodes in adjacent layers of the neural network to produce thedesired values at the output nodes. This process is sometimes referredto as deep learning.

Still referring to FIG. 6, machine-learning algorithms may include atleast a supervised machine-learning process 628. At least a supervisedmachine-learning process 628, as defined herein, include algorithms thatreceive a training set relating a number of inputs to a number ofoutputs, and seek to find one or more mathematical relations relatinginputs to outputs, where each of the one or more mathematical relationsis optimal according to some criterion specified to the algorithm usingsome scoring function. For instance, a supervised learning algorithm mayinclude inputs and outputs, and a scoring function representing adesired form of relationship to be detected between inputs and outputs;scoring function may, for instance, seek to maximize the probabilitythat a given input and/or combination of elements inputs is associatedwith a given output to minimize the probability that a given input isnot associated with a given output. Scoring function may be expressed asa risk function representing an “expected loss” of an algorithm relatinginputs to outputs, where loss is computed as an error functionrepresenting a degree to which a prediction generated by the relation isincorrect when compared to a given input-output pair provided intraining data 604. Persons skilled in the art, upon reviewing theentirety of this disclosure, will be aware of various possiblevariations of at least a supervised machine-learning process 628 thatmay be used to determine relation between inputs and outputs. Supervisedmachine-learning processes may include classification algorithms asdefined above.

Further referring to FIG. 6, machine learning processes may include atleast an unsupervised machine-learning processes 642. An unsupervisedmachine-learning process, as used herein, is a process that derivesinferences in datasets without regard to labels; as a result, anunsupervised machine-learning process may be free to discover anystructure, relationship, and/or correlation provided in the data.Unsupervised processes may not require a response variable; unsupervisedprocesses may be used to find interesting patterns and/or inferencesbetween variables, to determine a degree of correlation between two ormore variables, or the like.

Still referring to FIG. 6, machine-learning module 600 may be designedand configured to create a machine-learning model 624 using techniquesfor development of linear regression models. Linear regression modelsmay include ordinary least squares regression, which aims to minimizethe square of the difference between predicted outcomes and actualoutcomes according to an appropriate norm for measuring such adifference (e.g. a vector-space distance norm); coefficients of theresulting linear equation may be modified to improve minimization.Linear regression models may include ridge regression methods, where thefunction to be minimized includes the least-squares function plus termmultiplying the square of each coefficient by a scalar amount topenalize large coefficients. Linear regression models may include leastabsolute shrinkage and selection operator (LASSO) models, in which ridgeregression is combined with multiplying the least-squares term by afactor of 1 divided by double the number of samples. Linear regressionmodels may include a multi-task lasso model wherein the norm applied inthe least-squares term of the lasso model is the Frobenius normamounting to the square root of the sum of squares of all terms. Linearregression models may include the elastic net model, a multi-taskelastic net model, a least angle regression model, a LARS lasso model,an orthogonal matching pursuit model, a Bayesian regression model, alogistic regression model, a stochastic gradient descent model, aperceptron model, a passive aggressive algorithm, a robustnessregression model, a Huber regression model, or any other suitable modelthat may occur to persons skilled in the art upon reviewing the entiretyof this disclosure. Linear regression models may be generalized in anembodiment to polynomial regression models, whereby a polynomialequation (e.g. a quadratic, cubic or higher-order equation) providing abest predicted output/actual output fit is sought; similar methods tothose described above may be applied to minimize error functions, aswill be apparent to persons skilled in the art upon reviewing theentirety of this disclosure.

Continuing to refer to FIG. 6, machine-learning algorithms may include,without limitation, linear discriminant analysis. Machine-learningalgorithm may include quadratic discriminate analysis. Machine-learningalgorithms may include kernel ridge regression. Machine-learningalgorithms may include support vector machines, including withoutlimitation support vector classification-based regression processes.Machine-learning algorithms may include stochastic gradient descentalgorithms, including classification and regression algorithms based onstochastic gradient descent. Machine-learning algorithms may includenearest neighbors algorithms. Machine-learning algorithms may includeGaussian processes such as Gaussian Process Regression. Machine-learningalgorithms may include cross-decomposition algorithms, including partialleast squares and/or canonical correlation analysis. Machine-learningalgorithms may include naive Bayes methods. Machine-learning algorithmsmay include algorithms based on decision trees, such as decision treeclassification or regression algorithms. Machine-learning algorithms mayinclude ensemble methods such as bagging meta-estimator, forest ofrandomized tress, AdaBoost, gradient tree boosting, and/or votingclassifier methods. Machine-learning algorithms may include neural netalgorithms, including convolutional neural net processes.

Still referring to FIG. 6, models may be generated using alternative oradditional artificial intelligence methods, including without limitationby creating an artificial neural network, such as a convolutional neuralnetwork comprising an input layer of nodes, one or more intermediatelayers, and an output layer of nodes. Connections between nodes may becreated via the process of “training” the network, in which elementsfrom a training data 604 set are applied to the input nodes, a suitabletraining algorithm (such as Levenberg-Marquardt, conjugate gradient,simulated annealing, or other algorithms) is then used to adjust theconnections and weights between nodes in adjacent layers of the neuralnetwork to produce the desired values at the output nodes. This processis sometimes referred to as deep learning. This network may be trainedusing training data 604.

Referring now to FIG. 7, an embodiment of an electric aircraft 700 ispresented. Still referring to FIG. 7, electric aircraft 700 may includea vertical takeoff and landing aircraft (eVTOL). As used herein, avertical take-off and landing (eVTOL) aircraft is one that can hover,take off, and land vertically. An eVTOL, as used herein, is anelectrically powered aircraft typically using an energy source, of aplurality of energy sources to power the aircraft. In order to optimizethe power and energy necessary to propel the aircraft. eVTOL may becapable of rotor-based cruising flight, rotor-based takeoff, rotor-basedlanding, fixed-wing cruising flight, airplane-style takeoff,airplane-style landing, and/or any combination thereof. Rotor-basedflight, as described herein, is where the aircraft generated lift andpropulsion by way of one or more powered rotors coupled with an engine,such as a “quad copter,” multi-rotor helicopter, or other vehicle thatmaintains its lift primarily using downward thrusting propulsors.Fixed-wing flight, as described herein, is where the aircraft is capableof flight using wings and/or foils that generate life caused by theaircraft's forward airspeed and the shape of the wings and/or foils,such as airplane-style flight.

With continued reference to FIG. 7, a number of aerodynamic forces mayact upon the electric aircraft 700 during flight. Forces acting on anelectric aircraft 700 during flight may include, without limitation,thrust, the forward force produced by the rotating element of theelectric aircraft 700 and acts parallel to the longitudinal axis.Another force acting upon electric aircraft 700 may be, withoutlimitation, drag, which may be defined as a rearward retarding forcewhich is caused by disruption of airflow by any protruding surface ofthe electric aircraft 700 such as, without limitation, the wing, rotor,and fuselage. Drag may oppose thrust and acts rearward parallel to therelative wind. A further force acting upon electric aircraft 700 mayinclude, without limitation, weight, which may include a combined loadof the electric aircraft 700 itself, crew, baggage, and/or fuel. Weightmay pull electric aircraft 700 downward due to the force of gravity. Anadditional force acting on electric aircraft 700 may include, withoutlimitation, lift, which may act to oppose the downward force of weightand may be produced by the dynamic effect of air acting on the airfoiland/or downward thrust from the propulsor of the electric aircraft. Liftgenerated by the airfoil may depend on speed of airflow, density of air,total area of an airfoil and/or segment thereof, and/or an angle ofattack between air and the airfoil. For example, and without limitation,electric aircraft 700 are designed to be as lightweight as possible.Reducing the weight of the aircraft and designing to reduce the numberof components is essential to optimize the weight. To save energy, itmay be useful to reduce weight of components of an electric aircraft700, including without limitation propulsors and/or propulsionassemblies. In an embodiment, the motor may eliminate need for manyexternal structural features that otherwise might be needed to join onecomponent to another component. The motor may also increase energyefficiency by enabling a lower physical propulsor profile, reducing dragand/or wind resistance. This may also increase durability by lesseningthe extent to which drag and/or wind resistance add to forces acting onelectric aircraft 700 and/or propulsors.

It is to be noted that any one or more of the aspects and embodimentsdescribed herein may be conveniently implemented using one or moremachines (e.g., one or more computing devices that are utilized as auser computing device for an electronic document, one or more serverdevices, such as a document server, etc.) programmed according to theteachings of the present specification, as will be apparent to those ofordinary skill in the computer art. Appropriate software coding canreadily be prepared by skilled programmers based on the teachings of thepresent disclosure, as will be apparent to those of ordinary skill inthe software art. Aspects and implementations discussed above employingsoftware and/or software modules may also include appropriate hardwarefor assisting in the implementation of the machine executableinstructions of the software and/or software module.

Such software may be a computer program product that employs amachine-readable storage medium. A machine-readable storage medium maybe any medium that is capable of storing and/or encoding a sequence ofinstructions for execution by a machine (e.g., a computing device) andthat causes the machine to perform any one of the methodologies and/orembodiments described herein. Examples of a machine-readable storagemedium include, but are not limited to, a magnetic disk, an optical disc(e.g., CD, CD-R, DVD, DVD-R, etc.), a magneto-optical disk, a read-onlymemory “ROM” device, a random-access memory “RAM” device, a magneticcard, an optical card, a solid-state memory device, an EPROM, an EEPROM,and any combinations thereof. A machine-readable medium, as used herein,is intended to include a single medium as well as a collection ofphysically separate media, such as, for example, a collection of compactdiscs or one or more hard disk drives in combination with a computermemory. As used herein, a machine-readable storage medium does notinclude transitory forms of signal transmission.

Such software may also include information (e.g., data) carried as adata signal on a data carrier, such as a carrier wave. For example,machine-executable information may be included as a data-carrying signalembodied in a data carrier in which the signal encodes a sequence ofinstruction, or portion thereof, for execution by a machine (e.g., acomputing device) and any related information (e.g., data structures anddata) that causes the machine to perform any one of the methodologiesand/or embodiments described herein.

Examples of a computing device include, but are not limited to, anelectronic book reading device, a computer workstation, a terminalcomputer, a server computer, a handheld device (e.g., a tablet computer,a smartphone, etc.), a web appliance, a network router, a networkswitch, a network bridge, any machine capable of executing a sequence ofinstructions that specify an action to be taken by that machine, and anycombinations thereof. In one example, a computing device may includeand/or be included in a kiosk.

FIG. 8 shows a diagrammatic representation of one embodiment of acomputing device in the exemplary form of a computer system 800 withinwhich a set of instructions for causing a control system to perform anyone or more of the aspects and/or methodologies of the presentdisclosure may be executed. It is also contemplated that multiplecomputing devices may be utilized to implement a specially configuredset of instructions for causing one or more of the devices to performany one or more of the aspects and/or methodologies of the presentdisclosure. Computer system 800 includes a processor 804 and a memory808 that communicate with each other, and with other components, via abus 812. Bus 812 may include any of several types of bus structuresincluding, but not limited to, a memory bus, a memory controller, aperipheral bus, a local bus, and any combinations thereof, using any ofa variety of bus architectures.

Processor 804 may include any suitable processor, such as withoutlimitation a processor incorporating logical circuitry for performingarithmetic and logical operations, such as an arithmetic and logic unit(ALU), which may be regulated with a state machine and directed byoperational inputs from memory and/or sensors; processor 804 may beorganized according to Von Neumann and/or Harvard architecture as anon-limiting example. Processor 804 may include, incorporate, and/or beincorporated in, without limitation, a microcontroller, microprocessor,digital signal processor (DSP), Field Programmable Gate Array (FPGA),Complex Programmable Logic Device (CPLD), Graphical Processing Unit(GPU), general purpose GPU, Tensor Processing Unit (TPU), analog ormixed signal processor, Trusted Platform Module (TPM), a floating-pointunit (FPU), and/or system on a chip (SoC).

Memory 808 may include various components (e.g., machine-readable media)including, but not limited to, a random-access memory component, a readonly component, and any combinations thereof. In one example, a basicinput/output system 815 (BIOS), including basic routines that help totransfer information between elements within computer system 800, suchas during start-up, may be stored in memory 808. Memory 808 may alsoinclude (e.g., stored on one or more machine-readable media)instructions (e.g., software) 820 embodying any one or more of theaspects and/or methodologies of the present disclosure. In anotherexample, memory 808 may further include any number of program modulesincluding, but not limited to, an operating system, one or moreapplication programs, other program modules, program data, and anycombinations thereof.

Computer system 800 may also include a storage device 824. Examples of astorage device (e.g., storage device 824) include, but are not limitedto, a hard disk drive, a magnetic disk drive, an optical disc drive incombination with an optical medium, a solid-state memory device, and anycombinations thereof. Storage device 824 may be connected to bus 812 byan appropriate interface (not shown). Example interfaces include, butare not limited to, SCSI, advanced technology attachment (ATA), serialATA, universal serial bus (USB), IEEE 1394 (FIREWIRE), and anycombinations thereof. In one example, storage device 824 (or one or morecomponents thereof) may be removably interfaced with computer system 800(e.g., via an external port connector (not shown)). Particularly,storage device 824 and an associated machine-readable medium 828 mayprovide nonvolatile and/or volatile storage of machine-readableinstructions, data structures, program modules, and/or other data forcomputer system 800. In one example, software 820 may reside, completelyor partially, within machine-readable medium 828. In another example,software 820 may reside, completely or partially, within processor 804.

Computer system 800 may also include an input device 832. In oneexample, a user of computer system 800 may enter commands and/or otherinformation into computer system 800 via input device 832. Examples ofan input device 832 include, but are not limited to, an alpha-numericinput device (e.g., a keyboard), a pointing device, a joystick, agamepad, an audio input device (e.g., a microphone, a voice responsesystem, etc.), a cursor control device (e.g., a mouse), a touchpad, anoptical scanner, a video capture device (e.g., a still camera, a videocamera), a touchscreen, and any combinations thereof. Input device 832may be interfaced to bus 812 via any of a variety of interfaces (notshown) including, but not limited to, a serial interface, a parallelinterface, a game port, a USB interface, a FIREWIRE interface, a directinterface to bus 812, and any combinations thereof. Input device 832 mayinclude a touch screen interface that may be a part of or separate fromdisplay 835, discussed further below. Input device 832 may be utilizedas a user selection device for selecting one or more graphicalrepresentations in a graphical interface as described above.

A user may also input commands and/or other information to computersystem 800 via storage device 824 (e.g., a removable disk drive, a flashdrive, etc.) and/or network interface device 840. A network interfacedevice, such as network interface device 840, may be utilized forconnecting computer system 800 to one or more of a variety of networks,such as network 844, and one or more remote devices 848 connectedthereto. Examples of a network interface device include, but are notlimited to, a network interface card (e.g., a mobile network interfacecard, a LAN card), a modem, and any combination thereof. Examples of anetwork include, but are not limited to, a wide area network (e.g., theInternet, an enterprise network), a local area network (e.g., a networkassociated with an office, a building, a campus or other relativelysmall geographic space), a telephone network, a data network associatedwith a telephone/voice provider (e.g., a mobile communications providerdata and/or voice network), a direct connection between two computingdevices, and any combinations thereof. A network, such as network 844,may employ a wired and/or a wireless mode of communication. In general,any network topology may be used. Information (e.g., data, software 820,etc.) may be communicated to and/or from computer system 800 via networkinterface device 840.

Computer system 800 may further include a video display adapter 852 forcommunicating a displayable image to a display device, such as displaydevice 835. Examples of a display device include, but are not limitedto, a liquid crystal display (LCD), a cathode ray tube (CRT), a plasmadisplay, a light emitting diode (LED) display, and any combinationsthereof. Display adapter 852 and display device 835 may be utilized incombination with processor 804 to provide graphical representations ofaspects of the present disclosure. In addition to a display device,computer system 800 may include one or more other peripheral outputdevices including, but not limited to, an audio speaker, a printer, andany combinations thereof. Such peripheral output devices may beconnected to bus 812 via a peripheral interface 855. Examples of aperipheral interface include, but are not limited to, a serial port, aUSB connection, a FIREWIRE connection, a parallel connection, and anycombinations thereof.

The foregoing has been a detailed description of illustrativeembodiments of the invention. Various modifications and additions can bemade without departing from the spirit and scope of this invention.Features of each of the various embodiments described above may becombined with features of other described embodiments as appropriate inorder to provide a multiplicity of feature combinations in associatednew embodiments. Furthermore, while the foregoing describes a number ofseparate embodiments, what has been described herein is merelyillustrative of the application of the principles of the presentinvention. Additionally, although particular methods herein may beillustrated and/or described as being performed in a specific order, theordering is highly variable within ordinary skill to achieve methods,systems, and software according to the present disclosure. Accordingly,this description is meant to be taken only by way of example, and not tootherwise limit the scope of this invention.

Exemplary embodiments have been disclosed above and illustrated in theaccompanying drawings. It will be understood by those skilled in the artthat various changes, omissions and additions may be made to that whichis specifically disclosed herein without departing from the spirit andscope of the present invention.

What is claimed is:
 1. A system for flight control in electric aircraft,the system comprising: a flight controller, wherein the flightcontroller is configured to provide an initial vehicle torque signalcomprising a plurality of attitude commands; a mixer, wherein the mixerincludes circuitry configured to: receive the initial vehicle torquesignal; receive at least a vehicle torque limit; receive a plurality ofprioritization data, the plurality of prioritization data including aprioritization datum corresponding to each of the plurality of attitudecommands; determine a plurality of modified attitude commands as afunction of the at least a vehicle torque limit, the plurality ofattitude commands, and the plurality of prioritization data; generate,as a function of modified attitude commands, an output torque command,wherein the output torque command includes the initial vehicle torquesignal adjusted as a function of the at least a vehicle torque limit;and generate, as a function of the output torque command, a remainingvehicle torque.
 2. The system of claim 1, wherein the mixer comprises aninertia compensator.
 3. The system of claim 2, wherein the inertiacompensator comprises a lead filter.
 4. The system of claim 1, whereinthe mixer is implemented using an electrical logic circuit.
 5. Thesystem of claim 1, wherein the mixer is implemented using a processor.6. The system of claim 1, wherein the flight controller is aproportional-integral-derivative (PID) controller. The system of claim1, wherein the flight controller is configured to provide the initialvehicle torque signal as a function of a control algorithm.
 8. Thesystem of claim 1 wherein the flight controller is configured to providethe initial vehicle torque signal as a function of an autonomousfunction.
 9. The system of claim 1, wherein the remaining vehicle torquecomprises the remaining vehicle torque capability in an aircraft's pitchmoment.
 10. The system of claim 1, wherein the remaining vehicle torquecomprises the remaining vehicle torque capability in an aircraft's rollmoment.
 11. A method for flight control in electric aircraft, the methodcomprising: providing, at the flight controller, an initial vehicletorque signal comprising at least an attitude command; receiving, at themixer, the initial vehicle torque signal including a plurality ofattitude commands; receiving, at the mixer, at least a vehicle torquelimit; receiving, at the mixer, a plurality of prioritization dataincluding a prioritization datum corresponding to each of the pluralityof attitude commands; determining, at the mixer, a plurality of modifiedattitude commands as a function of the at least a vehicle torque limit,the plurality of attitude commands, and the plurality of prioritizationdata; generating, at the mixer, as a function of modified attitudecommands, an output torque command, wherein the output torque commandincludes the initial vehicle torque signal adjusted as a function of theat least a vehicle torque limit; generating, at the mixer, as a functionof the output torque command, a remaining vehicle torque.
 12. The methodof claim 1, wherein the mixer comprises an inertia compensator.
 13. Themethod of claim 2, wherein the inertia compensator comprises a leadfilter.
 14. The method of claim 1, wherein the mixer is implementedusing an electrical logic circuit.
 15. The method of claim 1, whereinthe mixer is implemented using a processor.
 16. The method of claim 1,wherein the flight controller is a proportional-integral-derivative(PID) controller.
 17. The method of claim 1, wherein providing theinitial vehicle torque signal further comprises: providing, using theflight controller, the initial vehicle torque signal as a function of acontrol algorithm.
 18. The method of claim 1 wherein providing theinitial vehicle torque signal further comprises: providing, using theflight controller, the initial vehicle torque signal as a function of anautonomous function.
 19. The method of claim 1, wherein the remainingvehicle torque comprises the remaining vehicle torque capability in anaircraft's yaw moment.
 20. The method of claim 1, wherein the remainingvehicle torque comprises the remaining vehicle torque capability in anaircraft's assisted lift.